OpenRaiser/Intern-Atlas huggingface dataset:
Intelligent multiscale simulation based on process-guided composite database
Venue: arXiv cs.CE 2020
Year: 2020

In the paper, we present an integrated data-driven modeling framework based on process modeling, material homogenization, mechanistic machine learning, and concurrent multiscale simulation. We are interested in the injection-molded short fiber reinforced composites, which have been identified as key material systems in automotive, aerospace, and electronics industries. The molding process induces spatially varying microstructures across various length scales, while the resulting strongly anisotropic and nonlinear material properties are still challenging to be captured by conventional modeling approaches. To prepare the linear elastic training data for our machine learning tasks, Representative Volume Elements (RVE) with different fiber orientations and volume fractions are generated through stochastic reconstruction. More importantly, we utilize the recently proposed Deep Material Network (DMN) to learn the hidden microscale morphologies from data. With essential physics embedded in its building blocks, this data-driven material model can be extrapolated to predict nonlinear material behaviors efficiently and accurately. Through the transfer learning of DMN, we create a unified process-guided material database that covers a full range of geometric descriptors for short fiber reinforced composites. Finally, this unified DMN database is implemented and coupled with macroscale finite element model to enable concurrent multiscale simulations. From our perspective, the proposed framework is also promising in many other emergent multiscale engineering systems, such as additive manufacturing and compressive molding.
A level set representation method for N-dimensional convex shape and applications
Venue: arXiv cs.CV 2020
Year: 2020

In this work, we present a new efficient method for convex shape representation, which is regardless of the dimension of the concerned objects, using level-set approaches. Convexity prior is very useful for object completion in computer vision. It is a very challenging task to design an efficient method for high dimensional convex objects representation. In this paper, we prove that the convexity of the considered object is equivalent to the convexity of the associated signed distance function. Then, the second order condition of convex functions is used to characterize the shape convexity equivalently. We apply this new method to two applications: object segmentation with convexity prior and convex hull problem (especially with outliers). For both applications, the involved problems can be written as a general optimization problem with three constraints. Efficient algorithm based on alternating direction method of multipliers is presented for the optimization problem. Numerical experiments are conducted to verify the effectiveness and efficiency of the proposed representation method and algorithm.
XTDrone: A Customizable Multi-Rotor UAVs Simulation Platform
Venue: arXiv cs.RO 2020
Year: 2020

A customizable multi-rotor UAVs simulation platform based on ROS, Gazebo and PX4 is presented. The platform, which is called XTDrone, integrates dynamic models, sensor models, control algorithm, state estimation algorithm, and 3D scenes. The platform supports multi UAVs and other robots. The platform is modular and each module can be modified, which means that users can test its own algorithm, such as SLAM, object detection, motion planning, attitude control, multi-UAV cooperation, and cooperation with other robots on the platform. The platform runs in lockstep, so the simulation speed can be adjusted according to the computer performance. In this paper, two cases, evaluating different visual SLAM algorithm and realizing UAV formation, are shown to demonstrate how the platform works.
Accelerating Deep Reinforcement Learning With the Aid of Partial Model: Energy-Efficient Predictive Video Streaming
Venue: arXiv cs.LG 2020
Year: 2020

Predictive power allocation is conceived for energy-efficient video streaming over mobile networks using deep reinforcement learning. The goal is to minimize the accumulated energy consumption of each base station over a complete video streaming session under the constraint that avoids video playback interruptions. To handle the continuous state and action spaces, we resort to deep deterministic policy gradient (DDPG) algorithm for solving the formulated problem. In contrast to previous predictive power allocation policies that first predict future information with historical data and then optimize the power allocation based on the predicted information, the proposed policy operates in an on-line and end-to-end manner. By judiciously designing the action and state that only depend on slowly-varying average channel gains, we reduce the signaling overhead between the edge server and the base stations, and make it easier to learn a good policy. To further avoid playback interruption throughout the learning process and improve the convergence speed, we exploit the partially known model of the system dynamics by integrating the concepts of safety layer, post-decision state, and virtual experiences into the basic DDPG algorithm. Our simulation results show that the proposed policies converge to the optimal policy that is derived based on perfect large-scale channel prediction and outperform the first-predict-then-optimize policy in the presence of prediction errors. By harnessing the partially known model, the convergence speed can be dramatically improved.
Prioritization of Basic Safety Message in DSRC Based on Distance to Danger
Venue: arXiv cs.NI 2020
Year: 2020

Many parties claim the technical significance of Dedicated Short-Range Communications (DSRC) in intelligent transportation system (ITS) for promotion of transportation safety. The main challenge in this key vehicle-to-everything (V2X) standard is high odds of network congestion. Furthermore, in accordance with a V2X network being inherently dynamic in key parameters such as vehicle density and velocity, the networking behavior of a DSRC system is usually highly complicated to analyze. In addition, the United States Federal Communications Commission (US FCC) recently announced the "5.9 GHz band reform," which reduced the dedicated bandwidth for DSRC to 10 MHz from 75 MHz. Motivated from these, the necessity of "lightening" the networking load of a DSRC network has become essential to keep safety-related operations without performance deterioration. To this end, this paper proposes a protocol that prioritizes transmission of a basic safety message (BSM) at a vehicle according to the level of danger that the vehicle experiences. The proposed protocol uses the distance between a danger source and a vehicle as the metric to determine the priority for transmission. Our results show that this protocol prioritizes the transmission opportunity to dangerous vehicles, and hence results in higher performance in terms of key metrics--i.e., average delay, throughput, and inter-reception time ($\mathsf{IRT}$).
Towards an Enterprise-Ready Implementation of Artificial Intelligence-Enabled, Blockchain-Based Smart Contracts
Venue: arXiv cs.DC 2020
Year: 2020

Blockchain technology and artificial intelligence (AI) are current hot topics in research and practice. However, the potentials of their combination have been studied just recently to a larger extend. While different use cases for combining AI and blockchain have been discussed, the idea of enabling blockchain-based smart contracts to perform "smarter" decisions by using AI or machine learning (ML) models has only been considered on the conceptual level so far. It remained open, how such AI-enabled smart contracts could be implemented in a robust way for real-world applications. Therefore, in this paper a new, enterprise-class implementation of AI-enabled smart contracts is presented and first insights regarding its feasibility are discussed.
Invariant Rationalization
Venue: arXiv cs.LG 2020
Year: 2020

Selective rationalization improves neural network interpretability by identifying a small subset of input features -- the rationale -- that best explains or supports the prediction. A typical rationalization criterion, i.e. maximum mutual information (MMI), finds the rationale that maximizes the prediction performance based only on the rationale. However, MMI can be problematic because it picks up spurious correlations between the input features and the output. Instead, we introduce a game-theoretic invariant rationalization criterion where the rationales are constrained to enable the same predictor to be optimal across different environments. We show both theoretically and empirically that the proposed rationales can rule out spurious correlations, generalize better to different test scenarios, and align better with human judgments. Our data and code are available.
Tracking an Auto-Regressive Process with Limited Communication per Unit Time
Venue: arXiv cs.IT 2020
Year: 2020

Samples from a high-dimensional AR[1] process are observed by a sender which can communicate only finitely many bits per unit time to a receiver. The receiver seeks to form an estimate of the process value at every time instant in real-time. We consider a time-slotted communication model in a slow-sampling regime where multiple communication slots occur between two sampling instants. We propose a successive update scheme which uses communication between sampling instants to refine estimates of the latest sample and study the following question: Is it better to collect communication of multiple slots to send better refined estimates, making the receiver wait more for every refinement, or to be fast but loose and send new information in every communication opportunity? We show that the fast but loose successive update scheme with ideal spherical codes is universally optimal asymptotically for a large dimension. However, most practical quantization codes for fixed dimensions do not meet the ideal performance required for this optimality, and they typically will have a bias in the form of a fixed additive error. Interestingly, our analysis shows that the fast but loose scheme is not an optimal choice in the presence of such errors, and a judiciously chosen frequency of updates outperforms it.
BS-NAS: Broadening-and-Shrinking One-Shot NAS with Searchable Numbers of Channels
Venue: arXiv cs.LG 2020
Year: 2020

One-Shot methods have evolved into one of the most popular methods in Neural Architecture Search (NAS) due to weight sharing and single training of a supernet. However, existing methods generally suffer from two issues: predetermined number of channels in each layer which is suboptimal; and model averaging effects and poor ranking correlation caused by weight coupling and continuously expanding search space. To explicitly address these issues, in this paper, a Broadening-and-Shrinking One-Shot NAS (BS-NAS) framework is proposed, in which `broadening' refers to broadening the search space with a spring block enabling search for numbers of channels during training of the supernet; while `shrinking' refers to a novel shrinking strategy gradually turning off those underperforming operations. The above innovations broaden the search space for wider representation and then shrink it by gradually removing underperforming operations, followed by an evolutionary algorithm to efficiently search for the optimal architecture. Extensive experiments on ImageNet illustrate the effectiveness of the proposed BS-NAS as well as the state-of-the-art performance.
Visual Question Answering for Cultural Heritage
Venue: arXiv cs.CV 2020
Year: 2020

Technology and the fruition of cultural heritage are becoming increasingly more entwined, especially with the advent of smart audio guides, virtual and augmented reality, and interactive installations. Machine learning and computer vision are important components of this ongoing integration, enabling new interaction modalities between user and museum. Nonetheless, the most frequent way of interacting with paintings and statues still remains taking pictures. Yet images alone can only convey the aesthetics of the artwork, lacking is information which is often required to fully understand and appreciate it. Usually this additional knowledge comes both from the artwork itself (and therefore the image depicting it) and from an external source of knowledge, such as an information sheet. While the former can be inferred by computer vision algorithms, the latter needs more structured data to pair visual content with relevant information. Regardless of its source, this information still must be be effectively transmitted to the user. A popular emerging trend in computer vision is Visual Question Answering (VQA), in which users can interact with a neural network by posing questions in natural language and receiving answers about the visual content. We believe that this will be the evolution of smart audio guides for museum visits and simple image browsing on personal smartphones. This will turn the classic audio guide into a smart personal instructor with which the visitor can interact by asking for explanations focused on specific interests. The advantages are twofold: on the one hand the cognitive burden of the visitor will decrease, limiting the flow of information to what the user actually wants to hear; and on the other hand it proposes the most natural way of interacting with a guide, favoring engagement.
Effects of Discretization of Decision and Objective Spaces on the Performance of Evolutionary Multiobjective Optimization Algorithms
Venue: arXiv cs.NE 2020
Year: 2020

Recently, the discretization of decision and objective spaces has been discussed in the literature. In some studies, it is shown that the decision space discretization improves the performance of evolutionary multi-objective optimization (EMO) algorithms on continuous multi-objective test problems. In other studies, it is shown that the objective space discretization improves the performance on combinatorial multi-objective problems. However, the effect of the simultaneous discretization of both spaces has not been examined in the literature. In this paper, we examine the effects of the decision space discretization, objective space discretization and simultaneous discretization on the performance of NSGA-II through computational experiments on the DTLZ and WFG problems. Using various settings about the number of decision variables and the number of objectives, our experiments are performed on four types of problems: standard problems, large-scale problems, many-objective problems, and large-scale many-objective problems. We show that the decision space discretization has a positive effect for large-scale problems and the objective space discretization has a positive effect for many-objective problems. We also show the discretization of both spaces is useful for large-scale many-objective problems.
A Better Variant of Self-Critical Sequence Training
Venue: arXiv cs.CV 2020
Year: 2020

In this work, we present a simple yet better variant of Self-Critical Sequence Training. We make a simple change in the choice of baseline function in REINFORCE algorithm. The new baseline can bring better performance with no extra cost, compared to the greedy decoding baseline.
Rapid Reviews in Software Engineering
Venue: arXiv cs.SE 2020
Year: 2020

Integrating research evidence into practice is one of the main goals of Evidence-Based Software Engineering (EBSE). Secondary studies, one of the main EBSE products, are intended to summarize the best research evidence and make them easily consumable by practitioners. However, recent studies show that some secondary studies lack connections with software engineering practice. In this chapter, we present the concept of Rapid Reviews, which are lightweight secondary studies focused on delivering evidence to practitioners in a timely manner. Rapid reviews support practitioners in their decision-making, and should be conducted bounded to a practical problem, inserted into a practical context. Thus, Rapid Reviews can be easily integrated in a knowledge/technology transfer initiative. After describing the basic concepts, we present the results and experiences of conducting two Rapid Reviews. We also provide guidelines to help researchers and practitioners who want to conduct Rapid Reviews, and we finally discuss topics that my concern the research community about the feasibility of Rapid Reviews as an Evidence-Based method. In conclusion, we believe Rapid Reviews might interest researchers and practitioners working in the intersection between software engineering research and practice.
Learning to Walk: Spike Based Reinforcement Learning for Hexapod Robot Central Pattern Generation
Venue: arXiv cs.NE 2020
Year: 2020

Learning to walk -- i.e., learning locomotion under performance and energy constraints continues to be a challenge in legged robotics. Methods such as stochastic gradient, deep reinforcement learning (RL) have been explored for bipeds, quadrupeds and hexapods. These techniques are computationally intensive and often prohibitive for edge applications. These methods rely on complex sensors and pre-processing of data, which further increases energy and latency. Recent advances in spiking neural networks (SNNs) promise a significant reduction in computing owing to the sparse firing of neuros and has been shown to integrate reinforcement learning mechanisms with biologically observed spike time dependent plasticity (STDP). However, training a legged robot to walk by learning the synchronization patterns of central pattern generators (CPG) in an SNN framework has not been shown. This can marry the efficiency of SNNs with synchronized locomotion of CPG based systems providing breakthrough end-to-end learning in mobile robotics. In this paper, we propose a reinforcement based stochastic weight update technique for training a spiking CPG. The whole system is implemented on a lightweight raspberry pi platform with integrated sensors, thus opening up exciting new possibilities.
Egalitarian solution for games with discrete side payment
Venue: arXiv cs.GT 2020
Year: 2020

In this paper, we study the egalitarian solution for games with discrete side payment, where the characteristic function is integer-valued and payoffs of players are integral vectors. The egalitarian solution, introduced by Dutta and Ray in 1989, is a solution concept for transferable utility cooperative games in characteristic form, which combines commitment for egalitarianism and promotion of indivisual interests in a consistent manner. We first point out that the nice properties of the egalitarian solution (in the continuous case) do not extend to games with discrete side payment. Then we show that the Lorenz stable set, which may be regarded a variant of the egalitarian solution, has nice properties such as the Davis and Maschler reduced game property and the converse reduced game property. For the proofs we utilize recent results in discrete convex analysis on decreasing minimization on an M-convex set investigated by Frank and Murota.
Fast and memory-optimal dimension reduction using Kac's walk
Venue: arXiv cs.DS 2020
Year: 2020

In this work, we analyze dimension reduction algorithms based on the Kac walk and discrete variants. (1) For $n$ points in $\mathbb{R}^{d}$, we design an optimal Johnson-Lindenstrauss (JL) transform based on the Kac walk which can be applied to any vector in time $O(d\log{d})$ for essentially the same restriction on $n$ as in the best-known transforms due to Ailon and Liberty [SODA, 2008], and Bamberger and Krahmer [arXiv, 2017]. Our algorithm is memory-optimal, and outperforms existing algorithms in regimes when $n$ is sufficiently large and the distortion parameter is sufficiently small. In particular, this confirms a conjecture of Ailon and Chazelle [STOC, 2006] in a stronger form. (2) The same construction gives a simple transform with optimal Restricted Isometry Property (RIP) which can be applied in time $O(d\log{d})$ for essentially the same range of sparsity as in the best-known such transform due to Ailon and Rauhut [Discrete Comput. Geom., 2014]. (3) We show that by fixing the angle in the Kac walk to be $π/4$ throughout, one obtains optimal JL and RIP transforms with almost the same running time, thereby confirming -- up to a $\log\log{d}$ factor -- a conjecture of Avron, Maymounkov, and Toledo [SIAM J. Sci. Comput., 2010]. Our moment-based analysis of this modification of the Kac walk may also be of independent interest.
JPEG Steganography and Synchronization of DCT Coefficients for a Given Development Pipeline
Venue: arXiv cs.MM 2020
Year: 2020

This short paper proposes to use the statistical analysis of the correlation between DCT coefficients to design a new synchronization strategy that can be used for cost-based steganographic schemes in the JPEG domain. First, an analysis is performed on the covariance matrix of DCT coefficients of neighboring blocks after a development similar to the one used to generate BossBase. This analysis exhibits groups of uncorrelated coefficients: 4 groups per block and 2 groups of uncorrelated diagonal neighbors together with groups of mutually correlated coefficients groups of 6 coefficients per blocs and 8 coefficients between 2 adjacent blocks. Using the uncorrelated groups, an embedding scheme can be designed using only 8 disjoint lattices. The cost map for each lattice is updated firstly by using an implicit underlying Gaussian distribution with a variance directly computed from the embedding costs, and secondly by deriving conditional distributions from multivariate distributions. The covariance matrix of these distributions takes into account both the correlations exhibited by the analysis of the covariance matrix and the variance derived from the cost. This synchronization scheme enables to obtain a gain of PE of 5% at QF 95 for an embedding rate close to 0.3 bnzac coefficient using DCTR feature sets.
Efficient Crowd Counting via Structured Knowledge Transfer
Venue: arXiv cs.CV 2020
Year: 2020

Crowd counting is an application-oriented task and its inference efficiency is crucial for real-world applications. However, most previous works relied on heavy backbone networks and required prohibitive run-time consumption, which would seriously restrict their deployment scopes and cause poor scalability. To liberate these crowd counting models, we propose a novel Structured Knowledge Transfer (SKT) framework, which fully exploits the structured knowledge of a well-trained teacher network to generate a lightweight but still highly effective student network. Specifically, it is integrated with two complementary transfer modules, including an Intra-Layer Pattern Transfer which sequentially distills the knowledge embedded in layer-wise features of the teacher network to guide feature learning of the student network and an Inter-Layer Relation Transfer which densely distills the cross-layer correlation knowledge of the teacher to regularize the student's feature evolutio Consequently, our student network can derive the layer-wise and cross-layer knowledge from the teacher network to learn compact yet effective features. Extensive evaluations on three benchmarks well demonstrate the effectiveness of our SKT for extensive crowd counting models. In particular, only using around $6\%$ of the parameters and computation cost of original models, our distilled VGG-based models obtain at least 6.5$\times$ speed-up on an Nvidia 1080 GPU and even achieve state-of-the-art performance. Our code and models are available at {\url{https://github.com/HCPLab-SYSU/SKT}}.