OpenRaiser/Intern-Atlas huggingface dataset:
Fault-Tolerant Routing in Hypercube Networks by Avoiding Faulty Nodes
Venue: arXiv cs.NI 2019
Year: 2019

Next to the high performance, the essential feature of the multiprocessor systems is their fault-tolerant capability. In this regard, fault-tolerant interconnection networks and especially fault-tolerant routing methods are crucial parts of these systems. Hypercube is a popular interconnection network that is used in many multiprocessors. There are several suggested practices for fault tolerant routing in these systems. In this paper, a neural routing method is introduced which is named as Fault Avoidance Routing (FAR). This method keeps the message as far from the faulty nodes as possible. The proposed method employs the Hopfield neural network. In comparison with other neural routing methods, FAR requires a small number of neurons. The simulation results show that FAR has excellent performance in larger interconnection networks and networks with a high density of faulty nodes.
Time series classification based on fractal properties
Venue: arXiv cs.NI 2019
Year: 2019

The article considers classification task of fractal time series by the meta algorithms based on decision trees. Binomial multiplicative stochastic cascades are used as input time series. Comparative analysis of the classification approaches based on different features is carried out. The results indicate the advantage of the machine learning methods over the traditional estimating the degree of self-similarity.
Cooperative Distributed Robust Control of Modular Mobile Robots with Bounded Curvature and Velocity
Venue: arXiv cs.RO 2019
Year: 2019

A novel motion control system for Compliant Framed wheeled Modular Mobile Robots (CFMMR) is studied in this paper. This type of wheeled mobile robot uses rigid axles coupled by compliant frame modules to provide both full suspension and enhanced steering capability without additional hardware. The proposed control system is developed by combining a bounded curvature-based kinematic controller and a nonlinear damping dynamic controller. In particular, multiple forms of controller interaction are examined. A twoaxle scout CFMMR configuration is used to evaluate the different control structures. Experimental results verify efficient motion control of posture regulation.
Implementing Efficient Message Logging Protocols as MPI Application Extensions
Venue: arXiv cs.DC 2019
Year: 2019

Message logging protocols are enablers of local rollback, a more efficient alternative to global rollback, for fault tolerant MPI applications. Until now, message logging MPI implementations have incurred the overheads of a redesign and redeployment of an MPI library, as well as continued performance penalties across various kernels. Successful research efforts for message logging implementations do exist, but not a single one of them can be easily deployed today by more than a few experts. In contrast, in this work we build efficient message logging capabilities on top of an MPI library with no message logging capabilities; we do so for two different HPC kernels, one with a global exchange pattern (CG), and one with a neighbourhood exchange pattern (LULESH). While our library of choice ULFM detects failure and recovers MPI communicators, we build on that to then restore the intra- and inter-process data consistency of both applications. This task turns out to be challenging, and we present the methodology for doing so in this work. In the end, we achieve message logging capabilities for each kernel, without the need for an actual message logging runtime underneath. On the performance side, we match state-of-the-art solutions and (a) eliminate event logging and the event logger component altogether, and (b) design a hybrid protocol, which gracefully shifts between global and local rollback, depending on the available payload logging memory. Such a hybrid protocol between local and global rollback has not been previously proposed to our knowledge. Our extensions span a few hundred lines of code for each kernel, are open-sourced, and enable local and global rollback after process failure.
Image-based reconstruction for the impact problems by using DPNNs
Venue: arXiv cs.OH 2019
Year: 2019

With the improvement of the pattern recognition and feature extraction of Deep Neural Networks (DPNNs), image-based design and optimization have been widely used in multidisciplinary researches. Recently, a Reconstructive Neural Network (ReConNN) has been proposed to obtain an image-based model from an analysis-based model [1, 2], and a steady-state heat transfer of a heat sink has been successfully reconstructed. Commonly, this method is suitable to handle stable-state problems. However, it has difficulties handling nonlinear transient impact problems, due to the bottlenecks of the Deep Neural Network (DPNN). For example, nonlinear transient problems make it difficult for the Generative Adversarial Network (GAN) to generate various reasonable images. Therefore, in this study, an improved ReConNN method is proposed to address the mentioned weaknesses. Time-dependent ordered images can be generated. Furthermore, the improved method is successfully applied in impact simulation case and engineering experiment. Through the experiments, comparisons and analyses, the improved method is demonstrated to outperform the former one in terms of its accuracy, efficiency and costs.
Generative Model with Dynamic Linear Flow
Venue: arXiv cs.LG 2019
Year: 2019

Flow-based generative models are a family of exact log-likelihood models with tractable sampling and latent-variable inference, hence conceptually attractive for modeling complex distributions. However, flow-based models are limited by density estimation performance issues as compared to state-of-the-art autoregressive models. Autoregressive models, which also belong to the family of likelihood-based methods, however suffer from limited parallelizability. In this paper, we propose Dynamic Linear Flow (DLF), a new family of invertible transformations with partially autoregressive structure. Our method benefits from the efficient computation of flow-based methods and high density estimation performance of autoregressive methods. We demonstrate that the proposed DLF yields state-of-theart performance on ImageNet 32x32 and 64x64 out of all flow-based methods, and is competitive with the best autoregressive model. Additionally, our model converges 10 times faster than Glow (Kingma and Dhariwal, 2018). The code is available at https://github.com/naturomics/DLF.
Reconstruction of Privacy-Sensitive Data from Protected Templates
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, we address the problem of data reconstruction from privacy-protected templates, based on recent concept of sparse ternary coding with ambiguization (STCA). The STCA is a generalization of randomization techniques which includes random projections, lossy quantization, and addition of ambiguization noise to satisfy the privacy-utility trade-off requirements. The theoretical privacy-preserving properties of STCA have been validated on synthetic data. However, the applicability of STCA to real data and potential threats linked to reconstruction based on recent deep reconstruction algorithms are still open problems. Our results demonstrate that STCA still achieves the claimed theoretical performance when facing deep reconstruction attacks for the synthetic i.i.d. data, while for real images special measures are required to guarantee proper protection of the templates.
Enhancing Cross-task Transferability of Adversarial Examples with Dispersion Reduction
Venue: arXiv cs.LG 2019
Year: 2019

Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i.e., they maintain their effectiveness even against other models. With great efforts delved into the transferability of adversarial examples, surprisingly, less attention has been paid to its impact on real-world deep learning deployment. In this paper, we investigate the transferability of adversarial examples across a wide range of real-world computer vision tasks, including image classification, explicit content detection, optical character recognition (OCR), and object detection. It represents the cybercriminal's situation where an ensemble of different detection mechanisms need to be evaded all at once. We propose practical attack that overcomes existing attacks' limitation of requiring task-specific loss functions by targeting on the `dispersion' of internal feature map. We report evaluation on four different computer vision tasks provided by Google Cloud Vision APIs to show how our approach outperforms existing attacks by degrading performance of multiple CV tasks by a large margin with only modest perturbations.
A deep learning approach for analyzing the composition of chemometric data
Venue: arXiv cs.LG 2019
Year: 2019

We propose novel deep learning based chemometric data analysis technique. We trained L2 regularized sparse autoencoder end-to-end for reducing the size of the feature vector to handle the classic problem of the curse of dimensionality in chemometric data analysis. We introduce a novel technique of automatic selection of nodes inside the hidden layer of an autoencoder through Pareto optimization. Moreover, Gaussian process regressor is applied on the reduced size feature vector for the regression. We evaluated our technique on orange juice and wine dataset and results are compared against 3 state-of-the-art methods. Quantitative results are shown on Normalized Mean Square Error (NMSE) and the results show considerable improvement in the state-of-the-art.
Frustratingly Easy Person Re-Identification: Generalizing Person Re-ID in Practice
Venue: arXiv cs.CV 2019
Year: 2019

Contemporary person re-identification (\reid) methods usually require access to data from the deployment camera network during training in order to perform well. This is because contemporary \reid{} models trained on one dataset do not generalise to other camera networks due to the domain-shift between datasets. This requirement is often the bottleneck for deploying \reid{} systems in practical security or commercial applications, as it may be impossible to collect this data in advance or prohibitively costly to annotate it. This paper alleviates this issue by proposing a simple baseline for domain generalizable~(DG) person re-identification. That is, to learn a \reid{} model from a set of source domains that is suitable for application to unseen datasets out-of-the-box, without any model updating. Specifically, we observe that the domain discrepancy in \reid{} is due to style and content variance across datasets and demonstrate appropriate Instance and Feature Normalization alleviates much of the resulting domain-shift in Deep \reid{} models. Instance Normalization~(IN) in early layers filters out style statistic variations and Feature Normalization~(FN) in deep layers is able to further eliminate disparity in content statistics. Compared to contemporary alternatives, this approach is extremely simple to implement, while being faster to train and test, thus making it an extremely valuable baseline for implementing \reid{} in practice. With a few lines of code, it increases the rank 1 \reid{} accuracy by {11.8\%, 33.2\%, 12.8\% and 8.5\%} on the VIPeR, PRID, GRID, and i-LIDS benchmarks respectively. Source codes are available at \url{https://github.com/BJTUJia/person_reID_DualNorm}.
Load Balancing Guardrails: Keeping Your Heavy Traffic on the Road to Low Response Times
Venue: arXiv cs.PF 2019
Year: 2019

Load balancing systems, comprising a central dispatcher and a scheduling policy at each server, are widely used in practice, and their response time has been extensively studied in the theoretical literature. While much is known about the scenario where the scheduling at the servers is First-Come-First-Served (FCFS), to minimize mean response time we must use Shortest-Remaining-Processing-Time (SRPT) scheduling at the servers. Much less is known about dispatching polices when SRPT scheduling is used. Unfortunately, traditional dispatching policies that are used in practice in systems with FCFS servers often have poor performance in systems with SRPT servers. In this paper, we devise a simple fix that can be applied to any dispatching policy. This fix, called guardrails, ensures that the dispatching policy yields optimal mean response time under heavy traffic when used in a system with SRPT servers. Any dispatching policy, when augmented with guardrails, becomes heavy-traffic optimal. Our results yield the first analytical bounds on mean response time for load balancing systems with SRPT scheduling at the servers.
Joint power and resource allocation of D2D communication with low-resolution ADC
Venue: arXiv cs.IT 2019
Year: 2019

This paper considers the joint power control and resource allocation for a device-to-device (D2D) underlay cellular system with a multi-antenna BS employing ADCs with different resolutions. We propose a four-step algorithm that optimizes the ADC resolution profile at the base station (BS) to reduce the energy consumption and perform joint power control and resource allocation of D2D communication users (DUEs) and cellular users (CUEs) to improve the D2D reliability.
Cycle-IR: Deep Cyclic Image Retargeting
Venue: arXiv cs.CV 2019
Year: 2019

Supervised deep learning techniques have achieved great success in various fields due to getting rid of the limitation of handcrafted representations. However, most previous image retargeting algorithms still employ fixed design principles such as using gradient map or handcrafted features to compute saliency map, which inevitably restricts its generality. Deep learning techniques may help to address this issue, but the challenging problem is that we need to build a large-scale image retargeting dataset for the training of deep retargeting models. However, building such a dataset requires huge human efforts. In this paper, we propose a novel deep cyclic image retargeting approach, called Cycle-IR, to firstly implement image retargeting with a single deep model, without relying on any explicit user annotations. Our idea is built on the reverse mapping from the retargeted images to the given input images. If the retargeted image has serious distortion or excessive loss of important visual information, the reverse mapping is unlikely to restore the input image well. We constrain this forward-reverse consistency by introducing a cyclic perception coherence loss. In addition, we propose a simple yet effective image retargeting network (IRNet) to implement the image retargeting process. Our IRNet contains a spatial and channel attention layer, which is able to discriminate visually important regions of input images effectively, especially in cluttered images. Given arbitrary sizes of input images and desired aspect ratios, our Cycle-IR can produce visually pleasing target images directly. Extensive experiments on the standard RetargetMe dataset show the superiority of our Cycle-IR. In addition, our Cycle-IR outperforms the Multiop method and obtains the best result in the user study. Code is available at https://github.com/mintanwei/Cycle-IR.
Learning Representations for Predicting Future Activities
Venue: arXiv cs.CV 2019
Year: 2019

Foreseeing the future is one of the key factors of intelligence. It involves understanding of the past and current environment as well as decent experience of its possible dynamics. In this work, we address future prediction at the abstract level of activities. We propose a network module for learning embeddings of the environment's dynamics in a self-supervised way. To take the ambiguities and high variances in the future activities into account, we use a multi-hypotheses scheme that can represent multiple futures. We demonstrate the approach by classifying future activities on the Epic-Kitchens and Breakfast datasets. Moreover, we generate captions that describe the future activities
A Topological Analysis of Communication Channels for Knowledge Sharing in Contemporary GitHub Projects
Venue: arXiv cs.SE 2019
Year: 2019

With over 28 million developers, success of the GitHub collaborative platform is highlighted through an abundance of communication channels among contemporary software projects. Knowledge is broken into two forms and its sharing (through communication channels) can be described as externalization or combination by the SECI model. Such platforms have revolutionized the way developers work, introducing new channels to share knowledge in the form of pull requests, issues and wikis. It is unclear how these channels capture and share knowledge. In this research, our goal is to analyze these communication channels in GitHub. First, using the SECI model, we are able to map how knowledge is shared through the communication channels. Then in a large-scale topology analysis of seven library package projects (i.e., involving over 70 thousand projects), we extracted insights of the different communication channels within GitHub. Using two research questions, we explored the evolution of the channels and adoption of channels by both popular and unpopular library package projects. Results show that (i) contemporary GitHub Projects tend to adopt multiple communication channels, (ii) communication channels change over time and (iii) communication channels are used to both capture new knowledge (i.e., externalization) and updating existing knowledge (i.e., combination).
HAPD aircraft Uncertain Norm-Bounded Mathematical model
Venue: arXiv cs.SY 2019
Year: 2019

In this paper an uncertain norm-bounded mathematical model for the UAV High Altitude Performance Demonstrator (HAPD) designed by Italian Aerospace Research Center (CIRA) is carried out. The linear state space description aims to describe the non-linear aircraft dynamic inside the operating envelope characterized by the following bounds: true air speed between 17 m/s and 23 m/s and altitude from 300 m to 700 m.
Space-bounded Church-Turing thesis and computational tractability of closed systems
Venue: arXiv cs.CC 2019
Year: 2019

We report a new limitation on the ability of physical systems to perform computation -- one that is based on generalizing the notion of memory, or storage space, available to the system to perform the computation. Roughly, we define memory as the maximal amount of information that the evolving system can carry from one instant to the next. We show that memory is a limiting factor in computation even in lieu of any time limitations on the evolving system - such as when considering its equilibrium regime. We call this limitation the Space-Bounded Church Turing Thesis (SBCT). The SBCT is supported by a Simulation Assertion (SA), which states that predicting the long-term behavior of bounded-memory systems is computationally tractable. In particular, one corollary of SA is an explicit bound on the computational hardness of the long-term behavior of a discrete-time finite-dimensional dynamical system that is affected by noise. We prove such a bound explicitly.
Simulating Problem Difficulty in Arithmetic Cognition Through Dynamic Connectionist Models
Venue: arXiv cs.NE 2019
Year: 2019

The present study aims to investigate similarities between how humans and connectionist models experience difficulty in arithmetic problems. Problem difficulty was operationalized by the number of carries involved in solving a given problem. Problem difficulty was measured in humans by response time, and in models by computational steps. The present study found that both humans and connectionist models experience difficulty similarly when solving binary addition and subtraction. Specifically, both agents found difficulty to be strictly increasing with respect to the number of carries. Another notable similarity is that problem difficulty increases more steeply in subtraction than in addition, for both humans and connectionist models. Further investigation on two model hyperparameters --- confidence threshold and hidden dimension --- shows higher confidence thresholds cause the model to take more computational steps to arrive at the correct answer. Likewise, larger hidden dimensions cause the model to take more computational steps to correctly answer arithmetic problems; however, this effect by hidden dimensions is negligible.
Improving Discrete Latent Representations With Differentiable Approximation Bridges
Venue: arXiv cs.LG 2019
Year: 2019

Modern neural network training relies on piece-wise (sub-)differentiable functions in order to use backpropagation to update model parameters. In this work, we introduce a novel method to allow simple non-differentiable functions at intermediary layers of deep neural networks. We do so by training with a differentiable approximation bridge (DAB) neural network which approximates the non-differentiable forward function and provides gradient updates during backpropagation. We present strong empirical results (performing over 600 experiments) in four different domains: unsupervised (image) representation learning, variational (image) density estimation, image classification, and sequence sorting to demonstrate that our proposed method improves state of the art performance. We demonstrate that training with DAB aided discrete non-differentiable functions improves image reconstruction quality and posterior linear separability by 10% against the Gumbel-Softmax relaxed estimator [37, 26] as well as providing a 9% improvement in the test variational lower bound in comparison to the state of the art RELAX [16] discrete estimator. We also observe an accuracy improvement of 77% in neural sequence sorting and a 25% improvement against the straight-through estimator [5] in an image classification setting. The DAB network is not used for inference and expands the class of functions that are usable in neural networks.
Accurate Visual Localization for Automotive Applications
Venue: arXiv cs.CV 2019
Year: 2019

Accurate vehicle localization is a crucial step towards building effective Vehicle-to-Vehicle networks and automotive applications. Yet standard grade GPS data, such as that provided by mobile phones, is often noisy and exhibits significant localization errors in many urban areas. Approaches for accurate localization from imagery often rely on structure-based techniques, and thus are limited in scale and are expensive to compute. In this paper, we present a scalable visual localization approach geared for real-time performance. We propose a hybrid coarse-to-fine approach that leverages visual and GPS location cues. Our solution uses a self-supervised approach to learn a compact road image representation. This representation enables efficient visual retrieval and provides coarse localization cues, which are fused with vehicle ego-motion to obtain high accuracy location estimates. As a benchmark to evaluate the performance of our visual localization approach, we introduce a new large-scale driving dataset based on video and GPS data obtained from a large-scale network of connected dash-cams. Our experiments confirm that our approach is highly effective in challenging urban environments, reducing localization error by an order of magnitude.
Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks
Venue: arXiv cs.CV 2019
Year: 2019

We present a project that aims to generate images that depict accurate, vivid, and personalized outcomes of climate change using Cycle-Consistent Adversarial Networks (CycleGANs). By training our CycleGAN model on street-view images of houses before and after extreme weather events (e.g. floods, forest fires, etc.), we learn a mapping that can then be applied to images of locations that have not yet experienced these events. This visual transformation is paired with climate model predictions to assess likelihood and type of climate-related events in the long term (50 years) in order to bring the future closer in the viewers mind. The eventual goal of our project is to enable individuals to make more informed choices about their climate future by creating a more visceral understanding of the effects of climate change, while maintaining scientific credibility by drawing on climate model projections.