OpenRaiser/Intern-Atlas huggingface dataset:
Learning to reinforcement learn for Neural Architecture Search
Venue: arXiv cs.NE 2019
Year: 2019

Reinforcement learning (RL) is a goal-oriented learning solution that has proven to be successful for Neural Architecture Search (NAS) on the CIFAR and ImageNet datasets. However, a limitation of this approach is its high computational cost, making it unfeasible to replay it on other datasets. Through meta-learning, we could bring this cost down by adapting previously learned policies instead of learning them from scratch. In this work, we propose a deep meta-RL algorithm that learns an adaptive policy over a set of environments, making it possible to transfer it to previously unseen tasks. The algorithm was applied to various proof-of-concept environments in the past, but we adapt it to the NAS problem. We empirically investigate the agent's behavior during training when challenged to design chain-structured neural architectures for three datasets with increasing levels of hardness, to later fix the policy and evaluate it on two unseen datasets of different difficulty. Our results show that, under resource constraints, the agent effectively adapts its strategy during training to design better architectures than the ones designed by a standard RL algorithm, and can design good architectures during the evaluation on previously unseen environments. We also provide guidelines on the applicability of our framework in a more complex NAS setting by studying the progress of the agent when challenged to design multi-branch architectures.
Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning
Venue: arXiv cs.RO 2019
Year: 2019

In this work, we propose a hierarchical reinforcement learning (HRL) structure which is capable of performing autonomous vehicle planning tasks in simulated environments with multiple sub-goals. In this hierarchical structure, the network is capable of 1) learning one task with multiple sub-goals simultaneously; 2) extracting attentions of states according to changing sub-goals during the learning process; 3) reusing the well-trained network of sub-goals for other similar tasks with the same sub-goals. The states are defined as processed observations which are transmitted from the perception system of the autonomous vehicle. A hybrid reward mechanism is designed for different hierarchical layers in the proposed HRL structure. Compared to traditional RL methods, our algorithm is more sample-efficient since its modular design allows reusing the policies of sub-goals across similar tasks. The results show that the proposed method converges to an optimal policy faster than traditional RL methods.
XceptionTime: A Novel Deep Architecture based on Depthwise Separable Convolutions for Hand Gesture Classification
Venue: arXiv cs.LG 2019
Year: 2019

Capitalizing on the need for addressing the existing challenges associated with gesture recognition via sparse multichannel surface Electromyography (sEMG) signals, the paper proposes a novel deep learning model, referred to as the XceptionTime architecture. The proposed innovative XceptionTime is designed by integration of depthwise separable convolutions, adaptive average pooling, and a novel non-linear normalization technique. At the heart of the proposed architecture is several XceptionTime modules concatenated in series fashion designed to capture both temporal and spatial information-bearing contents of the sparse multichannel sEMG signals without the need for data augmentation and/or manual design of feature extraction. In addition, through integration of adaptive average pooling, Conv1D, and the non-linear normalization approach, XceptionTime is less prone to overfitting, more robust to temporal translation of the input, and more importantly is independent from the input window size. Finally, by utilizing the depthwise separable convolutions, the XceptionTime network has far fewer parameters resulting in a less complex network. The performance of XceptionTime is tested on a sub Ninapro dataset, DB1, and the results showed a superior performance in comparison to any existing counterparts. In this regard, 5:71% accuracy improvement, on a window size 200ms, is reported in this paper, for the first time.
Generating dynamic contact graphs with indirect links
Venue: arXiv cs.SI 2019
Year: 2019

Graph models are widely used to study diffusion processes in contact networks. Recent data-driven research has highlighted the significance of indirect links, where interactions are possible when two nodes visit the same place at different times (SPDT), in determining network structure and diffusion dynamics. However, how to generate dynamic graphs with indirect links for modeling diffusion remains an unsolved challenge. Here, we present a dynamic contact graph model for generating contact networks with direct and indirect links. Our model introduces the concept of multiple concurrently active copies of a node for capturing indirect transmission links. The SPDT graph model builds on activity driven time-varying network modelling for generating dynamic contact networks using simple statistical distributions. This model is fitted with a large city-scale empirical dataset using maximum likelihood estimation methods. Finally, the performance of the model is evaluated by analysing the capability of capturing the network properties observed in empirical graphs constructed using the location updates of a social networking app and simulating SPDT diffusion processes. Our results show that, in comparison to current graph models that only include direct links, our graph model with indirect links match empirical network properties and diffusion dynamics much more closely.
Intelligent Reflecting Surface with Discrete Phase Shifts: Channel Estimation and Passive Beamforming
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we consider an intelligent reflecting surface (IRS)-aided single-user system where an IRS with discrete phase shifts is deployed to assist the uplink communication. A practical transmission protocol is proposed to execute channel estimation and passive beamforming successively. To minimize the mean square error (MSE) of channel estimation, we first formulate an optimization problem for designing the IRS reflection pattern in the training phase under the constraints of unit-modulus, discrete phase, and full rank. This problem, however, is NP-hard and thus difficult to solve in general. As such, we propose a low-complexity yet efficient method to solve it sub-optimally, by constructing a near-orthogonal reflection pattern based on either discrete Fourier transform (DFT)-matrix quantization or Hadamard-matrix truncation. Based on the estimated channel, we then formulate an optimization problem to maximize the achievable rate by designing the discrete-phase passive beamforming at the IRS with the training overhead and channel estimation error taken into account. To reduce the computational complexity of exhaustive search, we further propose a low-complexity successive refinement algorithm with a properly-designed initialization to obtain a high-quality suboptimal solution. Numerical results are presented to show the significant rate improvement of our proposed IRS training reflection pattern and passive beamforming designs as compared to other benchmark schemes.
Using Deep Neural Networks for Estimating Loop Unrolling Factor
Venue: arXiv cs.DC 2019
Year: 2019

Optimizing programs requires deep expertise. On one hand, it is a tedious task, because it requires a lot of tests to find out the best combination of optimizations to apply with their best factors. On the other hand, this task is critical, because it may degrade the performance of programs instead of improving it. The automatization of this task can deal with this problem and permit to obtain good results. Optimizing loops that take the most significant part of the program execution time plays a crucial role to achieve best performance. In this paper, we address Loop unrolling optimization, by proposing a deep Neural Network model to predict the optimal unrolling factor for programs written for TIRAMISU. TIRAMISU is a polyhedral framework designed to generate high performance code for multiple platforms including multicores, GPUs, and distributed machines. TIRAMISU introduces a scheduling language with novel commands to explicitly manage the complexities that arise when targeting these systems.
Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation
Venue: arXiv cs.CV 2019
Year: 2019

We study the problem of visual question answering (VQA) in images by exploiting supervised domain adaptation, where there is a large amount of labeled data in the source domain but only limited labeled data in the target domain with the goal to train a good target model. A straightforward solution is to fine-tune a pre-trained source model by using those limited labeled target data, but it usually cannot work well due to the considerable difference between the data distributions of the source and target domains. Moreover, the availability of multiple modalities (i.e., images, questions and answers) in VQA poses further challenges to model the transferability between those different modalities. In this paper, we tackle the above issues by proposing a novel supervised multi-modal domain adaptation method for VQA to learn joint feature embeddings across different domains and modalities. Specifically, we align the data distributions of the source and target domains by considering all modalities together as well as separately for each individual modality. Based on the extensive experiments on the benchmark VQA 2.0 and VizWiz datasets for the realistic open-ended VQA task, we demonstrate that our proposed method outperforms the existing state-of-the-art approaches in this challenging domain adaptation setting for VQA.
Learning to Order Sub-questions for Complex Question Answering
Venue: arXiv cs.CL 2019
Year: 2019

Answering complex questions involving multiple entities and relations is a challenging task. Logically, the answer to a complex question should be derived by decomposing the complex question into multiple simple sub-questions and then answering those sub-questions. Existing work has followed this strategy but has not attempted to optimize the order of how those sub-questions are answered. As a result, the sub-questions are answered in an arbitrary order, leading to larger search space and a higher risk of missing an answer. In this paper, we propose a novel reinforcement learning(RL) approach to answering complex questions that can learn a policy to dynamically decide which sub-question should be answered at each stage of reasoning. We lever-age the expected value-variance criterion to enable the learned policy to balance between the risk and utility of answering a sub-question. Experiment results show that the RL approach can substantially improve the optimality of ordering the sub-questions, leading to improved accuracy of question answering. The proposed method for learning to order sub-questions is general and can thus be potentially combined with many existing ideas for answering complex questions to enhance their performance.
Paired Domination versus Domination and Packing Number in Graphs
Venue: arXiv cs.DM 2019
Year: 2019

Given a graph $G=(V(G), E(G))$, the size of a minimum dominating set, minimum paired dominating set, and a minimum total dominating set of a graph $G$ are denoted by $γ(G)$, $γ_{\rm pr}(G)$, and $γ_{t}(G)$, respectively. For a positive integer $k$, a $k$-packing in $G$ is a set $S \subseteq V(G)$ such that for every pair of distinct vertices $u$ and $v$ in $S$, the distance between $u$ and $v$ is at least $k+1$. The $k$-packing number is the order of a largest $k$-packing and is denoted by $ρ_{k}(G)$. It is well known that $γ_{\rm pr}(G) \le 2γ(G)$. In this paper, we prove that it is NP-hard to determine whether $γ_{\rm pr}(G) = 2γ(G)$ even for bipartite graphs. We provide a simple characterization of trees with $γ_{\rm pr}(G) = 2γ(G)$, implying a polynomial-time recognition algorithm. We also prove that even for a bipartite graph, it is NP-hard to determine whether $γ_{\rm pr}(G)=γ_{t}(G)$. We finally prove that it is both NP-hard to determine whether $γ_{\rm pr}(G)=2ρ_{4}(G)$ and whether $γ_{\rm pr}(G)=2ρ_{3}(G)$.
Beyond Similarity: Relation Embedding with Dual Attentions for Item-based Recommendation
Venue: arXiv cs.IR 2019
Year: 2019

Given the effectiveness and ease of use, Item-based Collaborative Filtering (ICF) methods have been broadly used in industry in recent years. The key of ICF lies in the similarity measurement between items, which however is a coarse-grained numerical value that can hardly capture users' fine-grained preferences toward different latent aspects of items from a representation learning perspective. In this paper, we propose a model called REDA (latent Relation Embedding with Dual Attentions) to address this challenge. REDA is essentially a deep learning based recommendation method that employs an item relation embedding scheme through a neural network structure for inter-item relations representation. A relational user embedding is then proposed by aggregating the relation embeddings between all purchased items of a user, which not only better characterizes user preferences but also alleviates the data sparsity problem. Moreover, to capture valid meta-knowledge that reflects users' desired latent aspects and meanwhile suppress their explosive growth towards overfitting, we further propose a dual attentions mechanism, including a memory attention and a weight attention. A relation-wise optimization method is finally developed for model inference by constructing a personalized ranking loss for item relations. Extensive experiments are implemented on real-world datasets and the proposed model is shown to greatly outperform state-of-the-art methods, especially when the data is sparse.
Provable Computational and Statistical Guarantees for Efficient Learning of Continuous-Action Graphical Games
Venue: arXiv cs.GT 2019
Year: 2019

In this paper, we study the problem of learning the set of pure strategy Nash equilibria and the exact structure of a continuous-action graphical game with quadratic payoffs by observing a small set of perturbed equilibria. A continuous-action graphical game can possibly have an uncountable set of Nash euqilibria. We propose a $\ell_{12}-$ block regularized method which recovers a graphical game, whose Nash equilibria are the $ε$-Nash equilibria of the game from which the data was generated (true game). Under a slightly stringent condition on the parameters of the true game, our method recovers the exact structure of the graphical game. Our method has a logarithmic sample complexity with respect to the number of players. It also runs in polynomial time.
Deep Contextualized Self-training for Low Resource Dependency Parsing
Venue: arXiv cs.CL 2019
Year: 2019

Neural dependency parsing has proven very effective, achieving state-of-the-art results on numerous domains and languages. Unfortunately, it requires large amounts of labeled data, that is costly and laborious to create. In this paper we propose a self-training algorithm that alleviates this annotation bottleneck by training a parser on its own output. Our Deep Contextualized Self-training (DCST) algorithm utilizes representation models trained on sequence labeling tasks that are derived from the parser's output when applied to unlabeled data, and integrates these models with the base parser through a gating mechanism. We conduct experiments across multiple languages, both in low resource in-domain and in cross-domain setups, and demonstrate that DCST substantially outperforms traditional self-training as well as recent semi-supervised training methods.
Exoskeleton Knee Compliance Improves Gait Velocity and Stability in a Spinal Cord Injured User: A Case Report
Venue: arXiv cs.RO 2019
Year: 2019

Spinal cord injuries frequently impair the ability to walk. Powered lower limb exoskeletons offer a promising solution to restore walking ability. However, they are currently restricted to even ground. We hypothesized that compliant exoskeleton knees could decrease required effort to maneuver on uneven terrain, and increase gait velocity and stability. We describe a case study of a motor-complete spinal cord injury user (AIS A, Th12) walking with a powered exoskeleton on even and uneven ground over multiple sessions after extensive training. Measurements with compliant or rigid exoskeleton knee joints were performed on three different days for each configuration. Body motion and crutch ground interaction forces were recorded to assess gait performance. We observed higher walking speeds with a compliant exoskeleton knee configuration (mean: 0.116 m/s on uneven and 0.145 m/s on even ground) compared to a rigid configuration (mean: 0.083 m/s and 0.100 m/s). Crutch force impulse was significantly reduced in the compliant configuration. Lastly, gait was more symmetric when the knee joints were compliant. In conclusion, compliant exoskeleton knee joints can help maneuver uneven ground faster and with less user effort than rigid joints. Based on our findings, exoskeleton designers should consider introducing compliance into their design to increase gait robustness and performance, and render exoskeletons more suitable for daily life use.
GraphDefense: Towards Robust Graph Convolutional Networks
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, we study the robustness of graph convolutional networks (GCNs). Despite the good performance of GCNs on graph semi-supervised learning tasks, previous works have shown that the original GCNs are very unstable to adversarial perturbations. In particular, we can observe a severe performance degradation by slightly changing the graph adjacency matrix or the features of a few nodes, making it unsuitable for security-critical applications. Inspired by the previous works on adversarial defense for deep neural networks, and especially adversarial training algorithm, we propose a method called GraphDefense to defend against the adversarial perturbations. In addition, for our defense method, we could still maintain semi-supervised learning settings, without a large label rate. We also show that adversarial training in features is equivalent to adversarial training for edges with a small perturbation. Our experiments show that the proposed defense methods successfully increase the robustness of Graph Convolutional Networks. Furthermore, we show that with careful design, our proposed algorithm can scale to large graphs, such as Reddit dataset.
Enhancing User Experience in Virtual Reality with Radial Basis Function Interpolation Based Stereoscopic Camera Control
Venue: arXiv cs.GR 2019
Year: 2019

Providing a depth-rich Virtual Reality (VR) experience to users without causing discomfort remains to be a challenge with today's commercially available head-mounted displays (HMDs), which enforce strict measures on stereoscopic camera parameters for the sake of keeping visual discomfort to a minimum. However, these measures often lead to an unimpressive VR experience with shallow depth feeling. We propose the first method ready to be used with existing consumer HMDs for automated stereoscopic camera control in virtual environments (VEs). Using radial basis function interpolation and projection matrix manipulations, our method makes it possible to significantly enhance user experience in terms of overall perceived depth while maintaining visual discomfort on a par with the default arrangement. In our implementation, we also introduce the first immersive interface for authoring a unique 3D stereoscopic cinematography for any VE to be experienced with consumer HMDs. We conducted a user study that demonstrates the benefits of our approach in terms of superior picture quality and perceived depth. We also investigated the effects of using depth of field (DoF) in combination with our approach and observed that the addition of our DoF implementation was seen as a degraded experience, if not similar.
Variable-Scaling Rate Control for Collision-Free Teleoperation of an Unmanned Aerial Vehicle
Venue: arXiv cs.RO 2019
Year: 2019

We propose that automatically adjusting the scale factor in rate-control teleoperation could enable a human operator to better control the motion of a remote robot. In this paper, we present four new variable-scaling rate-control methods that adjust the scale factor depending on the state of the user's input commands and/or the risk of a collision between the robot and its environment. Methods that depend on the risk of a collision are designed to guarantee collision avoidance by setting the scale factor to be zero if the operator issues a command that would result in a collision between the robot and its environment. A within-subject user study was conducted to determine the effects of the four newly designed rate-control methods and a traditional fixed-scale rate-control method on a person's ability to complete a navigation task in a simulated two-dimensional environment. The results of this study indicate that well-designed variable-scale rate control can guarantee collision-free teleoperation without reducing task efficiency.
Throughput Analysis in Cache-enabled Millimeter Wave HetNets with Access and Backhaul Integration
Venue: arXiv cs.NI 2019
Year: 2019

Recently, a mmWave-based access and backhaul integration heterogeneous networks (HetNets) architecture (mABHetNets) has been envisioned to provide high wireless capacity.Since the access link and the backhaul link share the same mmwave spectral resource, a large spectrum bandwidth is occupied by the backhaul link, which hinders the wireless access capacity improvement. To overcome the backhaul spectrum occupation problem and improve the network throughput in the existing mABHetNets, we introduce the cache at base stations (BSs). In detail, by caching popular files at small base stations (SBSs),mABHetNets can offload the backhaul link traffic and transfer the redundant backhaul spectrum to the access link to increase the network throughout. However, introducing cache in SBSs will also incur additional power consumption and reduce the transmission power, which can lower the network throughput.In this paper, we investigate spectrum partition between the access link and the backhaul link as well as cache allocation to improve the network throughput in mABHetNets. With the stochastic geometry tool, we develop an analytical framework to characterize cache-enabled mABHetNets and obtain the signal-to-interference-plus-noise ratio (SINR) distributions in line-of-sight (LoS) and non-line-of-sight (NLoS) paths. Then we utilize the SINR distribution to derive the average potential throughput(APT). Extensive numerical results show that introducing cache can bring up to 80% APT gain to the existing mABHetNets.