OpenRaiser/Intern-Atlas huggingface dataset:
Traffic-aware Two-stage Queueing Communication Networks: Queue Analysis and Energy Saving
Venue: arXiv cs.NI 2020
Year: 2020

To boost energy saving for the general delay-tolerant IoT networks, a two-stage and single-relay queueing communication scheme is investigated. Concretely, a traffic-aware $N$-threshold and gated-service policy are applied at the relay. As two fundamental and significant performance metrics, the mean waiting time and long-term expected power consumption are explicitly derived and related with the queueing and service parameters, such as packet arrival rate, service threshold and channel statistics. Besides, we take into account the electrical circuit energy consumptions when the relay server and access point (AP) are in different modes and energy costs for mode transitions, whereby the power consumption model is more practical. The expected power minimization problem under the mean waiting time constraint is formulated. Tight closed-form bounds are adopted to obtain tractable analytical formulae with less computational complexity. The optimal energy-saving service threshold that can flexibly adjust to packet arrival rate is determined. In addition, numerical results reveal that: 1) sacrificing the mean waiting time not necessarily facilitates power savings; 2) a higher arrival rate leads to a greater optimal service threshold; and 3) our policy performs better than the current state-of-the-art.
Gaussians on Riemannian Manifolds: Applications for Robot Learning and Adaptive Control
Venue: arXiv cs.RO 2020
Year: 2020

This article presents an overview of robot learning and adaptive control applications that can benefit from a joint use of Riemannian geometry and probabilistic representations. The roles of Riemannian manifolds, geodesics and parallel transport in robotics are first discussed. Several forms of manifolds already employed in robotics are then presented, by also listing manifolds that have been underexploited but that have potentials in future robot learning applications. A varied range of techniques employing Gaussian distributions on Riemannian manifolds is then introduced, including clustering, regression, information fusion, planning and control problems. Two examples of applications are presented, involving the control of a prosthetic hand from surface electromyography (sEMG) data, and the teleoperation of a bimanual underwater robot. Further perspectives are finally discussed, with suggestions of promising research directions.
Lightweight Task Offloading Exploiting MPI Wait Times for Parallel Adaptive Mesh Refinement
Venue: arXiv cs.DC 2020
Year: 2020

Balancing the workload of sophisticated simulations is inherently difficult, since we have to balance both computational workload and memory footprint over meshes that can change any time or yield unpredictable cost per mesh entity, while modern supercomputers and their interconnects start to exhibit fluctuating performance. We propose a novel lightweight balancing technique for MPI+X to accompany traditional, prediction-based load balancing. It is a reactive diffusion approach that uses online measurements of MPI idle time to migrate tasks temporarily from overloaded to underemployed ranks. Tasks are deployed to ranks which otherwise would wait, processed with high priority, and made available to the overloaded ranks again. This migration is non-persistent. Our approach hijacks idle time to do meaningful work and is totally non-blocking, asynchronous and distributed without a global data view. Tests with a seismic simulation code developed in the ExaHyPE engine uncover the method's potential. We found speed-ups of up to 2-3 for ill-balanced scenarios without logical modifications of the code base and show that the strategy is capable to react quickly to temporarily changing workload or node performance.
MinneApple: A Benchmark Dataset for Apple Detection and Segmentation
Venue: arXiv cs.CV 2020
Year: 2020

In this work, we present a new dataset to advance the state-of-the-art in fruit detection, segmentation, and counting in orchard environments. While there has been significant recent interest in solving these problems, the lack of a unified dataset has made it difficult to compare results. We hope to enable direct comparisons by providing a large variety of high-resolution images acquired in orchards, together with human annotations of the fruit on trees. The fruits are labeled using polygonal masks for each object instance to aid in precise object detection, localization, and segmentation. Additionally, we provide data for patch-based counting of clustered fruits. Our dataset contains over 41, 000 annotated object instances in 1000 images. We present a detailed overview of the dataset together with baseline performance analysis for bounding box detection, segmentation, and fruit counting as well as representative results for yield estimation. We make this dataset publicly available and host a CodaLab challenge to encourage comparison of results on a common dataset. To download the data and learn more about MinneApple please see the project website: http://rsn.cs.umn.edu/index.php/MinneApple. Up to date information is available online.
MAD-TN: A Tool for Measuring Fluency in Human-Robot Collaboration
Venue: arXiv cs.AI 2020
Year: 2020

Fluency is an important metric in Human-Robot Interaction (HRI) that describes the coordination with which humans and robots collaborate on a task. Fluency is inherently linked to the timing of the task, making temporal constraint networks a promising way to model and measure fluency. We show that the Multi-Agent Daisy Temporal Network (MAD-TN) formulation, which expands on an existing concept of daisy-structured networks, is both an effective model of human-robot collaboration and a natural way to measure a number of existing fluency metrics. The MAD-TN model highlights new metrics that we hypothesize will strongly correlate with human teammates' perception of fluency.
A few filters are enough: Convolutional Neural Network for P300 Detection
Venue: arXiv cs.LG 2020
Year: 2020

Over the past decade, convolutional neural networks (CNNs) have become the driving force of an ever-increasing set of applications, achieving state-of-the-art performance. Most of the modern CNN architectures are composed of many convolutional and fully connected layers and typically require thousands or millions of parameters to learn. CNNs have also been effective in the detection of Event-Related Potentials from electroencephalogram (EEG) signals, notably the P300 component which is frequently employed in Brain-Computer Interfaces (BCIs). However, for this task, the increase in detection rates compared to approaches based on human-engineered features has not been as impressive as in other areas and might not justify such a large number of parameters. In this paper, we study the performances of existing CNN architectures with diverse complexities for single-trial within-subject and cross-subject P300 detection on four different datasets. We also proposed SepConv1D, a very simple CNN architecture consisting of a single depthwise separable 1D convolutional layer followed by a fully connected Sigmoid classification neuron. We found that with as few as four filters in its convolutional layer and a small overall number of parameters, SepConv1D obtained competitive performances in the four datasets. We believe this may represent an important step towards building simpler, cheaper, faster, and more portable BCIs.
Broken Metre: Attacking Resource Metering in EVM
Venue: arXiv cs.CR 2020
Year: 2020

Blockchain systems, such as Ethereum, use an approach called "metering" to assign a cost to smart contract execution, an approach which is designed to incentivise miners to operate the network and protect it against DoS attacks. In the past, the imperfections of Ethereum metering allowed several DoS attacks which were countered through modification of the metering mechanism. This paper presents a new DoS attack on Ethereum which systematically exploits its metering mechanism. We first replay and analyse several months of transactions, during which we discover a number of discrepancies in the metering model, such as significant inconsistencies in the pricing of the instructions. We further demonstrate that there is very little correlation between the execution cost and the utilised resources, such as CPU and memory. Based on these observations, we present a new type of DoS attack we call Resource Exhaustion Attack, which uses these imperfections to generate low-throughput contracts. To do this, we design a genetic algorithm that generates contracts with a throughput on average 200 times slower than typical contracts. We then show that all major Ethereum client implementations are vulnerable and, if running on commodity hardware, would be unable to stay in sync with the network when under attack. We argue that such an attack could be financially attractive not only for Ethereum competitors and speculators, but also for Ethereum miners. Finally, we discuss short-term and potential long-term fixes against such attacks. Our attack has been responsibly disclosed to the Ethereum Foundation and awarded a bug bounty reward of 5,000 USD.
BAFFLE : Blockchain Based Aggregator Free Federated Learning
Venue: arXiv cs.LG 2020
Year: 2020

A key aspect of Federated Learning (FL) is the requirement of a centralized aggregator to maintain and update the global model. However, in many cases orchestrating a centralized aggregator might be infeasible due to numerous operational constraints. In this paper, we introduce BAFFLE, an aggregator free, blockchain driven, FL environment that is inherently decentralized. BAFFLE leverages Smart Contracts (SC) to coordinate the round delineation, model aggregation and update tasks in FL. BAFFLE boosts computational performance by decomposing the global parameter space into distinct chunks followed by a score and bid strategy. In order to characterize the performance of BAFFLE, we conduct experiments on a private Ethereum network and use the centralized and aggregator driven methods as our benchmark. We show that BAFFLE significantly reduces the gas costs for FL on the blockchain as compared to a direct adaptation of the aggregator based method. Our results also show that BAFFLE achieves high scalability and computational efficiency while delivering similar accuracy as the benchmark methods.
Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning
Venue: arXiv cs.LG 2020
Year: 2020

Continuous control tasks in reinforcement learning are important because they provide an important framework for learning in high-dimensional state spaces with deceptive rewards, where the agent can easily become trapped into suboptimal solutions. One way to avoid local optima is to use a population of agents to ensure coverage of the policy space, yet learning a population with the "best" coverage is still an open problem. In this work, we present a novel approach to population-based RL in continuous control that leverages properties of normalizing flows to perform attractive and repulsive operations between current members of the population and previously observed policies. Empirical results on the MuJoCo suite demonstrate a high performance gain for our algorithm compared to prior work, including Soft-Actor Critic (SAC).
Inverse Visual Question Answering with Multi-Level Attentions
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we propose a novel deep multi-level attention model to address inverse visual question answering. The proposed model generates regional visual and semantic features at the object level and then enhances them with the answer cue by using attention mechanisms. Two levels of multiple attentions are employed in the model, including the dual attention at the partial question encoding step and the dynamic attention at the next question word generation step. We evaluate the proposed model on the VQA V1 dataset. It demonstrates state-of-the-art performance in terms of multiple commonly used metrics.
Configuration-Space Flipper Planning on 3D Terrain
Venue: arXiv cs.RO 2020
Year: 2020

Flippers are essential components of tracked robot locomotion systems for unstructured terrain, especially within a rescue scenario. Achieving full and semi-autonomy for such rescue robots is the goal of many research efforts. In this work, we propose an algorithm to plan the morphologies of a small rescue robot with four flippers over 3D ground without any extra sensor, such as pressure sensor. To achieve the goal, we simplify the rescue robot as a skeleton on inflated terrain. Its morphology can be represented by configurations of several parameters. Then we plan the mobile movement on 3D terrain with four individually manipulated flippers. We perform real robot experiments on three different obstacles. The results show that we move the flippers very effectively and are thus able to tackle those terrains very well.
Character-Centric Storytelling
Venue: arXiv cs.CL 2020
Year: 2020

Sequential vision-to-language or visual storytelling has recently been one of the areas of focus in computer vision and language modeling domains. Though existing models generate narratives that read subjectively well, there could be cases when these models miss out on generating stories that account and address all prospective human and animal characters in the image sequences. Considering this scenario, we propose a model that implicitly learns relationships between provided characters and thereby generates stories with respective characters in scope. We use the VIST dataset for this purpose and report numerous statistics on the dataset. Eventually, we describe the model, explain the experiment and discuss our current status and future work.
Visualizing Movement Control Optimization Landscapes
Venue: arXiv cs.LG 2020
Year: 2020

A large body of animation research focuses on optimization of movement control, either as action sequences or policy parameters. However, as closed-form expressions of the objective functions are often not available, our understanding of the optimization problems is limited. Building on recent work on analyzing neural network training, we contribute novel visualizations of high-dimensional control optimization landscapes; this yields insights into why control optimization is hard and why common practices like early termination and spline-based action parameterizations make optimization easier. For example, our experiments show how trajectory optimization can become increasingly ill-conditioned with longer trajectories, but parameterizing control as partial target states---e.g., target angles converted to torques using a PD-controller---can act as an efficient preconditioner. Both our visualizations and quantitative empirical data also indicate that neural network policy optimization scales better than trajectory optimization for long planning horizons. Our work advances the understanding of movement optimization and our visualizations should also provide value in educational use.
Toward Efficient Evaluation of Logic Encryption Schemes: Models and Metrics
Venue: arXiv cs.CR 2020
Year: 2020

Research in logic encryption over the last decade has resulted in various techniques to prevent different security threats such as Trojan insertion, intellectual property leakage, and reverse engineering. However, there is little agreement on a uniform set of metrics and models to efficiently assess the achieved security level and the trade-offs between security and overhead. This paper addresses the above challenges by relying on a general logic encryption model that can encompass all the existing techniques, and a uniform set of metrics that can capture multiple, possibly conflicting, security concerns. We apply our modeling approach to four state-of-the-art encryption techniques, showing that it enables fast and accurate evaluation of design trade-offs, average prediction errors that are at least 2X smaller than previous approaches, and the evaluation of compound encryption methods.
Ensemble Knowledge Distillation for Learning Improved and Efficient Networks
Venue: arXiv cs.CV 2020
Year: 2020

Ensemble models comprising of deep Convolutional Neural Networks (CNN) have shown significant improvements in model generalization but at the cost of large computation and memory requirements. In this paper, we present a framework for learning compact CNN models with improved classification performance and model generalization. For this, we propose a CNN architecture of a compact student model with parallel branches which are trained using ground truth labels and information from high capacity teacher networks in an ensemble learning fashion. Our framework provides two main benefits: i) Distilling knowledge from different teachers into the student network promotes heterogeneity in feature learning at different branches of the student network and enables the network to learn diverse solutions to the target problem. ii) Coupling the branches of the student network through ensembling encourages collaboration and improves the quality of the final predictions by reducing variance in the network outputs. Experiments on the well established CIFAR-10 and CIFAR-100 datasets show that our Ensemble Knowledge Distillation (EKD) improves classification accuracy and model generalization especially in situations with limited training data. Experiments also show that our EKD based compact networks outperform in terms of mean accuracy on the test datasets compared to state-of-the-art knowledge distillation based methods.
CAMAL: Context-Aware Multi-layer Attention framework for Lightweight Environment Invariant Visual Place Recognition
Venue: arXiv cs.CV 2020
Year: 2020

In the last few years, Deep Convolutional Neural Networks (D-CNNs) have shown state-of-the-art (SOTA) performance for Visual Place Recognition (VPR), a pivotal component of long-term intelligent robotic vision (vision-aware localization and navigation systems). The prestigious generalization power of D-CNNs gained upon training on large scale places datasets and learned persistent image regions which are found to be robust for specific place recognition under changing conditions and camera viewpoints. However, against the computation and power intensive D-CNNs based VPR algorithms that are employed to determine the approximate location of resource-constrained mobile robots, lightweight VPR techniques are preferred. This paper presents a computation- and energy-efficient CAMAL framework that captures place-specific multi-layer convolutional attentions efficient for environment invariant-VPR. At 4x lesser power consumption, evaluating the proposed VPR framework on challenging benchmark place recognition datasets reveal better and comparable Area under Precision-Recall (AUC-PR) curves with approximately 4x improved image retrieval performance over the contemporary VPR methodologies.
How Does Batch Normalization Help Binary Training?
Venue: arXiv cs.LG 2020
Year: 2020

Binary Neural Networks (BNNs) are difficult to train, and suffer from drop of accuracy. It appears in practice that BNNs fail to train in the absence of Batch Normalization (BatchNorm) layer. We find the main role of BatchNorm is to avoid exploding gradients in the case of BNNs. This finding suggests that the common initialization methods developed for full-precision networks are irrelevant to BNNs. We build a theoretical study on the role of BatchNorm in binary training, backed up by numerical experiments.
Weighted Linear Bandits for Non-Stationary Environments
Venue: arXiv cs.LG 2020
Year: 2020

We consider a stochastic linear bandit model in which the available actions correspond to arbitrary context vectors whose associated rewards follow a non-stationary linear regression model. In this setting, the unknown regression parameter is allowed to vary in time. To address this problem, we propose D-LinUCB, a novel optimistic algorithm based on discounted linear regression, where exponential weights are used to smoothly forget the past. This involves studying the deviations of the sequential weighted least-squares estimator under generic assumptions. As a by-product, we obtain novel deviation results that can be used beyond non-stationary environments. We provide theoretical guarantees on the behavior of D-LinUCB in both slowly-varying and abruptly-changing environments. We obtain an upper bound on the dynamic regret that is of order d^{2/3} B\_T^{1/3}T^{2/3}, where B\_T is a measure of non-stationarity (d and T being, respectively, dimension and horizon). This rate is known to be optimal. We also illustrate the empirical performance of D-LinUCB and compare it with recently proposed alternatives in simulated environments.
A nonlocal feature-driven exemplar-based approach for image inpainting
Venue: arXiv cs.CV 2020
Year: 2020

We present a nonlocal variational image completion technique which admits simultaneous inpainting of multiple structures and textures in a unified framework. The recovery of geometric structures is achieved by using general convolution operators as a measure of behavior within an image. These are combined with a nonlocal exemplar-based approach to exploit the self-similarity of an image in the selected feature domains and to ensure the inpainting of textures. We also introduce an anisotropic patch distance metric to allow for better control of the feature selection within an image and present a nonlocal energy functional based on this metric. Finally, we derive an optimization algorithm for the proposed variational model and examine its validity experimentally with various test images.
A Generalized TDoA/ToA Model for ToF Positioning
Venue: arXiv cs.NI 2020
Year: 2020

Many applications require positioning. Time of Flight (ToF) methods calculate distances by measuring the propagation time of signals. We present a novel ToF localization method. Our new approach works infrastructure-less, without pre-defined roles like Anchors or Tags. It generalizes existing synchronization-less Time Difference of Arrival (TDoA) and Time of Arrival (ToA) algorithms. We show how known algorithms can be derived from our new method. A major advantage of our approach is that it provides a comparable or better clock error robustness, i.e. the typical errors of crystal oscillators have negligible impact for TDoA and ToA measurements. We show that our channel usage is for most cases superior compared to the state-of-the art.
Understanding Architectures Learnt by Cell-based Neural Architecture Search
Venue: arXiv cs.LG 2020
Year: 2020

Neural architecture search (NAS) searches architectures automatically for given tasks, e.g., image classification and language modeling. Improving the search efficiency and effectiveness have attracted increasing attention in recent years. However, few efforts have been devoted to understanding the generated architectures. In this paper, we first reveal that existing NAS algorithms (e.g., DARTS, ENAS) tend to favor architectures with wide and shallow cell structures. These favorable architectures consistently achieve fast convergence and are consequently selected by NAS algorithms. Our empirical and theoretical study further confirms that their fast convergence derives from their smooth loss landscape and accurate gradient information. Nonetheless, these architectures may not necessarily lead to better generalization performance compared with other candidate architectures in the same search space, and therefore further improvement is possible by revising existing NAS algorithms.