OpenRaiser/Intern-Atlas huggingface dataset:
LFRic: Meeting the challenges of scalability and performance portability in Weather and Climate models
Venue: arXiv cs.DC 2019
Year: 2019

This paper describes LFRic: the new weather and climate modelling system being developed by the UK Met Office to replace the existing Unified Model in preparation for exascale computing in the 2020s. LFRic uses the GungHo dynamical core and runs on a semi-structured cubed-sphere mesh. The design of the supporting infrastructure follows object orientated principles to facilitate modularity and the use of external libraries where possible. In particular, a `separation of concerns' between the science code and parallel code is imposed to promote performance portability. An application called PSyclone, developed at the STFC Hartree centre, can generate the parallel code enabling deployment of a single source science code onto different machine architectures. This paper provides an overview of the scientific requirement, the design of the software infrastructure, and examples of PSyclone usage. Preliminary performance results show strong scaling and an indication that hybrid MPI/OpenMP performs better than pure MPI.
Evolving SDN for Low-Power IoT Networks
Venue: arXiv cs.NI 2019
Year: 2019

Software Defined Networking (SDN) offers a flexible and scalable architecture that abstracts decision making away from individual devices and provides a programmable network platform. However, implementing a centralized SDN architecture within the constraints of a low-power wireless network faces considerable challenges. Not only is controller traffic subject to jitter due to unreliable links and network contention, but the overhead generated by SDN can severely affect the performance of other traffic. This paper addresses the challenge of bringing high-overhead SDN architecture to IEEE 802.15.4 networks. We explore how traditional SDN needs to evolve in order to overcome the constraints of low-power wireless networks, and discuss protocol and architectural optimizations necessary to reduce SDN control overhead - the main barrier to successful implementation. We argue that interoperability with the existing protocol stack is necessary to provide a platform for controller discovery and coexistence with legacy networks. We consequently introduce μSDN, a lightweight SDN framework for Contiki, with both IPv6 and underlying routing protocol interoperability, as well as optimizing a number of elements within the SDN architecture to reduce control overhead to practical levels. We evaluate μSDN in terms of latency, energy, and packet delivery. Through this evaluation we show how the cost of SDN control overhead (both bootstrapping and management) can be reduced to a point where comparable performance and scalability is achieved against an IEEE 802.15.4-2012 RPL-based network. Additionally, we demonstrate μSDN through simulation: providing a use-case where the SDN configurability can be used to provide Quality of Service (QoS) for critical network flows experiencing interference, and we achieve considerable reductions in delay and jitter in comparison to a scenario without SDN.
DynaSOAr: Accelerating Single-Method Multiple-Objects Applications on GPUs
Venue: arXiv cs.PL 2019
Year: 2019

Object-oriented programming (OOP) has long been regarded as too inefficient for SIMD high-performance computing, despite the fact that many important HPC applications have an inherent object structure. We discovered a broad subset of OOP that can be implemented efficiently on massively parallel SIMD accelerators. We call it Single-Method Multiple-Objects (SMMO), because parallelism is expressed by running a method on all objects of a type. To make fast GPU programming available to domain experts who are less experienced in GPU programming, we developed DynaSOAr, a CUDA framework for SMMO applications. DynaSOAr improves the usage of allocated memory with an SOA data layout and achieves low memory fragmentation through efficient management of free and allocated memory blocks with lock-free, hierarchical bitmaps.
Sim-to-Real Transfer of Robot Learning with Variable Length Inputs
Venue: arXiv cs.LG 2019
Year: 2019

Current end-to-end deep Reinforcement Learning (RL) approaches require jointly learning perception, decision-making and low-level control from very sparse reward signals and high-dimensional inputs, with little capability of incorporating prior knowledge. This results in prohibitively long training times for use on real-world robotic tasks. Existing algorithms capable of extracting task-level representations from high-dimensional inputs, e.g. object detection, often produce outputs of varying lengths, restricting their use in RL methods due to the need for neural networks to have fixed length inputs. In this work, we propose a framework that combines deep sets encoding, which allows for variable-length abstract representations, with modular RL that utilizes these representations, decoupling high-level decision making from low-level control. We successfully demonstrate our approach on the robot manipulation task of object sorting, showing that this method can learn effective policies within mere minutes of highly simplified simulation. The learned policies can be directly deployed on a robot without further training, and generalize to variations of the task unseen during training.
A Novel Probabilistic Framework to Study the Impact of PV-battery Systems on Low-Voltage Distribution Networks
Venue: arXiv cs.SY 2019
Year: 2019

Battery storage, particularly residential battery storage coupled with rooftop PV, is emerging as an essential component of the smart grid technology mix. However, including battery storage and other flexible resources like electric vehicles and loads with thermal inertia into a probabilistic analysis based on Monte Carlo (MC) simulation is challenging, because their operational profiles are determined by computationally intensive optimization. Additionally, MC analysis requires a large pool of statistically-representative demand profiles to sample from. As a result, the analysis of the network impact of PV-battery systems has attracted little attention in the existing literature. To fill these knowledge gaps, this paper proposes a novel probabilistic framework to study the impact of PV-battery systems on low-voltage distribution networks. Specifically, the framework incorporates home energy management(HEM) operational decisions within the MC time series power flow analysis. First, using available smart meter data, we use a Bayesian nonparametric model to generate statistically-representative synthetic demand and PV profiles. Second, a policy function approximation that emulates battery scheduling decisions is used to make the simulation of optimization-based HEM feasible within the MC framework. The efficacy of our method is demonstrated on three representative low-voltage feeders, where the computation time to execute our MC framework is 5% of that when using explicit optimization methods in each MC sample. The assessment results show that uncoordinated battery scheduling has a limited beneficial impact, which is against the conjecture that batteries will serendipitously mitigate the technical problems induced by PV generation.
The 2018 PIRM Challenge on Perceptual Image Super-resolution
Venue: arXiv cs.CV 2019
Year: 2019

This paper reports on the 2018 PIRM challenge on perceptual super-resolution (SR), held in conjunction with the Perceptual Image Restoration and Manipulation (PIRM) workshop at ECCV 2018. In contrast to previous SR challenges, our evaluation methodology jointly quantifies accuracy and perceptual quality, therefore enabling perceptual-driven methods to compete alongside algorithms that target PSNR maximization. Twenty-one participating teams introduced algorithms which well-improved upon the existing state-of-the-art methods in perceptual SR, as confirmed by a human opinion study. We also analyze popular image quality measures and draw conclusions regarding which of them correlates best with human opinion scores. We conclude with an analysis of the current trends in perceptual SR, as reflected from the leading submissions.
OpenMPL: An Open Source Layout Decomposer
Venue: arXiv cs.OH 2019
Year: 2019

Multiple patterning lithography has been widely adopted in advanced technology nodes of VLSI manufacturing. As a key step in the design flow, multiple patterning layout decomposition (MPLD) is critical to design closure. Due to the NP-hardness of the general decomposition problem, various efficient algorithms have been proposed with high quality solutions. However, with increasingly complicated design flow and peripheral processing steps, developing a high-quality layout decomposer becomes more and more difficult, slowing down the further advancement in this field. This paper presents OpenMPL [1], an open-source layout decomposition framework, with well-separated peripheral processing and the core solving steps. We demonstrate the flexibility of the framework with efficient implementations of various state-of-the-art algorithms, which enable us to reproduce most of the recent results on widely-recognized benchmarks. We believe OpenMPL can pave the road for developing layout decomposition engines and stimulate further researches on this problem.
Throughput-Improving Control of Highways Facing Stochastic Perturbations
Venue: arXiv cs.SY 2019
Year: 2019

In this paper, we study the problem of traffic management in highways facing stochastic perturbations. To model the macroscopic traffic flow under perturbations, we use cell-transmission model with Markovian capacities. The decision variables are: (i) the admissible flow through each on-ramp, and (ii) whether individual on-ramps are metered to prioritize the mainline traffic or not. The objective is to maximize the throughput while ensuring that on-ramp queues remain bounded on average. We develop a computational approach to solving this stability-constrained, throughput-maximization problem. We establish a mixed-integer linear program for throughput maximization and construct an algorithm that gives the optimal solution in particular settings. We illustrate the performance benefits of the proposed approach through a computational study on a segment of Interstate 210 in California, USA.
Model Predictive Controller with Average Emissions Constraints for Diesel Airpath
Venue: arXiv cs.SY 2019
Year: 2019

Diesel airpath controllers are required to deliver good tracking performance whilst satisfying operational constraints and physical limitations of the actuators. Due to explicit constraint handling capabilities, model predictive controllers (MPC) have been successfully deployed in diesel airpath applications. Previous MPC implementations have considered instantaneous constraints on engine-out emissions in order to meet legislated emissions regulations. However, the emissions standards are specified over a drive cycle, and hence, can be satisfied on average rather than just instantaneously, potentially allowing the controller to exploit the trade-off between emissions and fuel economy. In this work, an MPC is formulated to maximise the fuel efficiency whilst tracking boost pressure and exhaust gas recirculation (EGR) rate references, and in the face of uncertainties, adhering to the input, safety constraints and constraints on emissions averaged over some finite time period. The tracking performance and satisfaction of average emissions constraints using the proposed controller are demonstrated through an experimental study considering the new European drive cycle.
Bugs in Infrastructure as Code
Venue: arXiv cs.SE 2019
Year: 2019

Infrastructure as code (IaC) scripts are used to automate the maintenance and configuration of software development and deployment infrastructure. IaC scripts can be complex in nature, containing hundreds of lines of code, leading to defects that can be difficult to debug, and lead to wide-scale system discrepancies such as service outages at scale. Use of IaC scripts is getting increasingly popular, yet the nature of defects that occur in these scripts have not been systematically categorized. A systematic categorization of defects can inform practitioners about process improvement opportunities to mitigate defects in IaC scripts. The goal of this paper is to help software practitioners improve their development process of infrastructure as code (IaC) scripts by categorizing the defect categories in IaC scripts based upon a qualitative analysis of commit messages and issue report descriptions. We mine open source version control systems collected from four organizations namely, Mirantis, Mozilla, Openstack, and Wikimedia Commons to conduct our research study. We use 1021, 3074, 7808, and 972 commits that map to 165, 580, 1383, and 296 IaC scripts, respectively, collected from Mirantis, Mozilla, Openstack, and Wikimedia Commons. With 89 raters we apply the defect type attribute of the orthogonal defect classification (ODC) methodology to categorize the defects. We also review prior literature that have used ODC to categorize defects, and compare the defect category distribution of IaC scripts with 26 non-IaC software systems. Respectively, for Mirantis, Mozilla, Openstack, and Wikimedia Commons, we observe (i) 49.3%, 36.5%, 57.6%, and 62.7% of the IaC defects to contain syntax and configuration-related defects; (ii) syntax and configuration-related defects are more prevalent amongst IaC scripts compared to that of previously-studied non-IaC software.
GAPLE: Generalizable Approaching Policy LEarning for Robotic Object Searching in Indoor Environment
Venue: arXiv cs.RO 2019
Year: 2019

We study the problem of learning a generalizable action policy for an intelligent agent to actively approach an object of interest in an indoor environment solely from its visual inputs. While scene-driven or recognition-driven visual navigation has been widely studied, prior efforts suffer severely from the limited generalization capability. In this paper, we first argue the object searching task is environment dependent while the approaching ability is general. To learn a generalizable approaching policy, we present a novel solution dubbed as GAPLE which adopts two channels of visual features: depth and semantic segmentation, as the inputs to the policy learning module. The empirical studies conducted on the House3D dataset as well as on a physical platform in a real world scenario validate our hypothesis, and we further provide in-depth qualitative analysis.
Implementation of Fuzzy C-Means and Possibilistic C-Means Clustering Algorithms, Cluster Tendency Analysis and Cluster Validation
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, several two-dimensional clustering scenarios are given. In those scenarios, soft partitioning clustering algorithms (Fuzzy C-means (FCM) and Possibilistic c-means (PCM)) are applied. Afterward, VAT is used to investigate the clustering tendency visually, and then in order of checking cluster validation, three types of indices (e.g., PC, DI, and DBI) were used. After observing the clustering algorithms, it was evident that each of them has its limitations; however, PCM is more robust to noise than FCM as in case of FCM a noise point has to be considered as a member of any of the cluster.
BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains
Venue: arXiv cs.HC 2019
Year: 2019

We present BrainNet which, to our knowledge, is the first multi-person non-invasive direct brain-to-brain interface for collaborative problem solving. The interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver information noninvasively to the brain. The interface allows three human subjects to collaborate and solve a task using direct brain-to-brain communication. Two of the three subjects are "Senders" whose brain signals are decoded using real-time EEG data analysis to extract decisions about whether to rotate a block in a Tetris-like game before it is dropped to fill a line. The Senders' decisions are transmitted via the Internet to the brain of a third subject, the "Receiver," who cannot see the game screen. The decisions are delivered to the Receiver's brain via magnetic stimulation of the occipital cortex. The Receiver integrates the information received and makes a decision using an EEG interface about either turning the block or keeping it in the same position. A second round of the game gives the Senders one more chance to validate and provide feedback to the Receiver's action. We evaluated the performance of BrainNet in terms of (1) Group-level performance during the game; (2) True/False positive rates of subjects' decisions; (3) Mutual information between subjects. Five groups of three subjects successfully used BrainNet to perform the Tetris task, with an average accuracy of 0.813. Furthermore, by varying the information reliability of the Senders by artificially injecting noise into one Sender's signal, we found that Receivers are able to learn which Sender is more reliable based solely on the information transmitted to their brains. Our results raise the possibility of future brain-to-brain interfaces that enable cooperative problem solving by humans using a "social network" of connected brains.
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
Venue: arXiv cs.NI 2019
Year: 2019

As a key technique for enabling artificial intelligence, machine learning (ML) is capable of solving complex problems without explicit programming. Motivated by its successful applications to many practical tasks like image recognition, both industry and the research community have advocated the applications of ML in wireless communication. This paper comprehensively surveys the recent advances of the applications of ML in wireless communication, which are classified as: resource management in the MAC layer, networking and mobility management in the network layer, and localization in the application layer. The applications in resource management further include power control, spectrum management, backhaul management, cache management, beamformer design and computation resource management, while ML based networking focuses on the applications in clustering, base station switching control, user association and routing. Moreover, literatures in each aspect is organized according to the adopted ML techniques. In addition, several conditions for applying ML to wireless communication are identified to help readers decide whether to use ML and which kind of ML techniques to use, and traditional approaches are also summarized together with their performance comparison with ML based approaches, based on which the motivations of surveyed literatures to adopt ML are clarified. Given the extensiveness of the research area, challenges and unresolved issues are presented to facilitate future studies, where ML based network slicing, infrastructure update to support ML based paradigms, open data sets and platforms for researchers, theoretical guidance for ML implementation and so on are discussed.
Neural Transductive Learning and Beyond: Morphological Generation in the Minimal-Resource Setting
Venue: arXiv cs.CL 2019
Year: 2019

Neural state-of-the-art sequence-to-sequence (seq2seq) models often do not perform well for small training sets. We address paradigm completion, the morphological task of, given a partial paradigm, generating all missing forms. We propose two new methods for the minimal-resource setting: (i) Paradigm transduction: Since we assume only few paradigms available for training, neural seq2seq models are able to capture relationships between paradigm cells, but are tied to the idiosyncracies of the training set. Paradigm transduction mitigates this problem by exploiting the input subset of inflected forms at test time. (ii) Source selection with high precision (SHIP): Multi-source models which learn to automatically select one or multiple sources to predict a target inflection do not perform well in the minimal-resource setting. SHIP is an alternative to identify a reliable source if training data is limited. On a 52-language benchmark dataset, we outperform the previous state of the art by up to 9.71% absolute accuracy.
Scenic: A Language for Scenario Specification and Scene Generation
Venue: arXiv cs.PL 2019
Year: 2019

We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning. Specifically, we consider the problems of training a perception system to handle rare events, testing its performance under different conditions, and debugging failures. We show how a probabilistic programming language can help address these problems by specifying distributions encoding interesting types of inputs and sampling these to generate specialized training and test sets. More generally, such languages can be used for cyber-physical systems and robotics to write environment models, an essential prerequisite to any formal analysis. In this paper, we focus on systems like autonomous cars and robots, whose environment is a "scene", a configuration of physical objects and agents. We design a domain-specific language, Scenic, for describing "scenarios" that are distributions over scenes. As a probabilistic programming language, Scenic allows assigning distributions to features of the scene, as well as declaratively imposing hard and soft constraints over the scene. We develop specialized techniques for sampling from the resulting distribution, taking advantage of the structure provided by Scenic's domain-specific syntax. Finally, we apply Scenic in a case study on a convolutional neural network designed to detect cars in road images, improving its performance beyond that achieved by state-of-the-art synthetic data generation methods.
Improved Parallel Cache-Oblivious Algorithms for Dynamic Programming and Linear Algebra
Venue: arXiv cs.DS 2019
Year: 2019

Emerging non-volatile main memory (NVRAM) technologies provide byte-addressability, low idle power, and improved memory-density, and are likely to be a key component in the future memory hierarchy. However, a critical challenge in achieving high performance is in accounting for the asymmetry that NVRAM writes can be significantly more expensive than NVRAM reads. In this paper, we consider a large class of cache-oblivious algorithms for dynamic programming (DP) and linear algebra, and try to reduce the writes in the asymmetric setting while maintaining high parallelism. To achieve that, our key approach is to show the correspondence between these problems and an abstraction for their computation, which is referred to as the $k$-d grids. Then by showing lower bound and new algorithms for computing $k$-d grids, we show a list of improved cache-oblivious algorithms of many DP recurrences and in linear algebra in the asymmetric setting, both sequentially and in parallel. Surprisingly, even without considering the read-write asymmetry (i.e., setting the write cost to be the same as the read cost in the algorithms), the new algorithms improve the existing cache complexity of many problems. We believe the reason is that the extra level of abstraction of $k$-d grids helps us to better understand the complexity and difficulties of these problems. We believe that the novelty of our framework is of interests and leads to many new questions for future work.