OpenRaiser/Intern-Atlas huggingface dataset:
STReSSD: Sim-To-Real from Sound for Stochastic Dynamics
Venue: arXiv cs.RO 2020
Year: 2020

Sound is an information-rich medium that captures dynamic physical events. This work presents STReSSD, a framework that uses sound to bridge the simulation-to-reality gap for stochastic dynamics, demonstrated for the canonical case of a bouncing ball. A physically-motivated noise model is presented to capture stochastic behavior of the balls upon collision with the environment. A likelihood-free Bayesian inference framework is used to infer the parameters of the noise model, as well as a material property called the coefficient of restitution, from audio observations. The same inference framework and the calibrated stochastic simulator are then used to learn a probabilistic model of ball dynamics. The predictive capabilities of the dynamics model are tested in two robotic experiments. First, open-loop predictions anticipate probabilistic success of bouncing a ball into a cup. The second experiment integrates audio perception with a robotic arm to track and deflect a bouncing ball in real-time. We envision that this work is a step towards integrating audio-based inference for dynamic robotic tasks. Experimental results can be viewed at https://youtu.be/b7pOrgZrArk.
Leveraging an Efficient and Semantic Location Embedding to Seek New Ports of Bike Share Services
Venue: arXiv cs.LG 2020
Year: 2020

For short distance traveling in crowded urban areas, bike share services are becoming popular owing to the flexibility and convenience. To expand the service coverage, one of the key tasks is to seek new service ports, which requires to well understand the underlying features of the existing service ports. In this paper, we propose a new model, named for Efficient and Semantic Location Embedding (ESLE), which carries both geospatial and semantic information of the geo-locations. To generate ESLE, we first train a multi-label model with a deep Convolutional Neural Network (CNN) by feeding the static map-tile images and then extract location embedding vectors from the model. Compared to most recent relevant literature, ESLE is not only much cheaper in computation, but also easier to interpret via a systematic semantic analysis. Finally, we apply ESLE to seek new service ports for NTT DOCOMO's bike share services operated in Japan. The initial results demonstrate the effectiveness of ESLE, and provide a few insights that might be difficult to discover by using the conventional approaches.
Learning Power Control for Cellular Systems with Heterogeneous Graph Neural Network
Venue: arXiv cs.LG 2020
Year: 2020

Optimizing power control in multi-cell cellular networks with deep learning enables such a non-convex problem to be implemented in real-time. When channels are time-varying, the deep neural networks (DNNs) need to be re-trained frequently, which calls for low training complexity. To reduce the number of training samples and the size of DNN required to achieve good performance, a promising approach is to embed the DNNs with priori knowledge. Since cellular networks can be modelled as a graph, it is natural to employ graph neural networks (GNNs) for learning, which exhibit permutation invariance (PI) and equivalence (PE) properties. Unlike the homogeneous GNNs that have been used for wireless problems, whose outputs are invariant or equivalent to arbitrary permutations of vertexes, heterogeneous GNNs (HetGNNs), which are more appropriate to model cellular networks, are only invariant or equivalent to some permutations. If the PI or PE properties of the HetGNN do not match the property of the task to be learned, the performance degrades dramatically. In this paper, we show that the power control policy has a combination of different PI and PE properties, and existing HetGNN does not satisfy these properties. We then design a parameter sharing scheme for HetGNN such that the learned relationship satisfies the desired properties. Simulation results show that the sample complexity and the size of designed GNN for learning the optimal power control policy in multi-user multi-cell networks are much lower than the existing DNNs, when achieving the same sum rate loss from the numerically obtained solutions.
Real-Time Control over Wireless Networks
Venue: arXiv cs.NI 2020
Year: 2020

Industrial internet of Things (IIoT) are gaining popularity for use in large-scale applications such as oil-field management (e.g., $74\times 8$km$^2$ East Texas Oil-field), smart farming, smart manufacturing, smart grid, and data center power management. These applications require the wireless stack to provide a scalable, reliable, low-power and low-latency communication. To realize a predictable and reliable communication in a highly unreliable wireless environment, industrial wireless standards use a centralized wireless stack design. In a centralized wireless stack design, a central manager generates routes and a communication schedule for a multi-channel time division multiple access communication (TDMA) based medium access control (MAC). However, a centralized wireless stack design is highly energy consuming, not scalable, and does not support frequent changes to networks or workloads. To address these challenges, the following contributions are made in this dissertation: (1) A scalable and distributed routing algorithm for industrial IoT which generates graph routes, which offer a high degree of redundancy, (2) A local and online scheduling algorithm that is scalable, energy-efficient, and supports network/workload dynamics while ensuring reliability and real-time performance, (3) An approach to minimize latency for in-band integration of multiple low-power networks, (4) A fast and efficient test of schedulability that determines if an application meets the real-time performance requirement for given network topology, and (5) A distributed scheduling and control co-design that balances the control performance requirement and real-time performance for industrial IoT.
From Dataset Recycling to Multi-Property Extraction and Beyond
Venue: arXiv cs.CL 2020
Year: 2020

This paper investigates various Transformer architectures on the WikiReading Information Extraction and Machine Reading Comprehension dataset. The proposed dual-source model outperforms the current state-of-the-art by a large margin. Next, we introduce WikiReading Recycled-a newly developed public dataset and the task of multiple property extraction. It uses the same data as WikiReading but does not inherit its predecessor's identified disadvantages. In addition, we provide a human-annotated test set with diagnostic subsets for a detailed analysis of model performance.
Efficient image retrieval using multi neural hash codes and bloom filters
Venue: arXiv cs.CV 2020
Year: 2020

This paper aims to deliver an efficient and modified approach for image retrieval using multiple neural hash codes and limiting the number of queries using bloom filters by identifying false positives beforehand. Traditional approaches involving neural networks for image retrieval tasks tend to use higher layers for feature extraction. But it has been seen that the activations of lower layers have proven to be more effective in a number of scenarios. In our approach, we have leveraged the use of local deep convolutional neural networks which combines the powers of both the features of lower and higher layers for creating feature maps which are then compressed using PCA and fed to a bloom filter after binary sequencing using a modified multi k-means approach. The feature maps obtained are further used in the image retrieval process in a hierarchical coarse-to-fine manner by first comparing the images in the higher layers for semantically similar images and then gradually moving towards the lower layers searching for structural similarities. While searching, the neural hashes for the query image are again calculated and queried in the bloom filter which tells us whether the query image is absent in the set or maybe present. If the bloom filter doesn't necessarily rule out the query, then it goes into the image retrieval process. This approach can be particularly helpful in cases where the image store is distributed since the approach supports parallel querying.
Conversion of LSAT behavioral specifications to automata
Venue: arXiv cs.FL 2020
Year: 2020

The Logistics Specification and Analysis Tool (LSAT) is a model-based engineering tool used for manufacturing system design and analysis. Using a domain specific language, a system can be specified in LSAT. In this paper, a conversion method is presented to obtain the system behavior of an LSAT specification in automata structure.
OP-IMS @ DIACR-Ita: Back to the Roots: SGNS+OP+CD still rocks Semantic Change Detection
Venue: arXiv cs.CL 2020
Year: 2020

We present the results of our participation in the DIACR-Ita shared task on lexical semantic change detection for Italian. We exploit one of the earliest and most influential semantic change detection models based on Skip-Gram with Negative Sampling, Orthogonal Procrustes alignment and Cosine Distance and obtain the winning submission of the shared task with near to perfect accuracy .94. Our results once more indicate that, within the present task setup in lexical semantic change detection, the traditional type-based approaches yield excellent performance.
Corpora Compared: The Case of the Swedish Gigaword & Wikipedia Corpora
Venue: arXiv cs.CL 2020
Year: 2020

In this work, we show that the difference in performance of embeddings from differently sourced data for a given language can be due to other factors besides data size. Natural language processing (NLP) tasks usually perform better with embeddings from bigger corpora. However, broadness of covered domain and noise can play important roles. We evaluate embeddings based on two Swedish corpora: The Gigaword and Wikipedia, in analogy (intrinsic) tests and discover that the embeddings from the Wikipedia corpus generally outperform those from the Gigaword corpus, which is a bigger corpus. Downstream tests will be required to have a definite evaluation.
Generative adversarial training of product of policies for robust and adaptive movement primitives
Venue: arXiv cs.RO 2020
Year: 2020

In learning from demonstrations, many generative models of trajectories make simplifying assumptions of independence. Correctness is sacrificed in the name of tractability and speed of the learning phase. The ignored dependencies, which often are the kinematic and dynamic constraints of the system, are then only restored when synthesizing the motion, which introduces possibly heavy distortions. In this work, we propose to use those approximate trajectory distributions as close-to-optimal discriminators in the popular generative adversarial framework to stabilize and accelerate the learning procedure. The two problems of adaptability and robustness are addressed with our method. In order to adapt the motions to varying contexts, we propose to use a product of Gaussian policies defined in several parametrized task spaces. Robustness to perturbations and varying dynamics is ensured with the use of stochastic gradient descent and ensemble methods to learn the stochastic dynamics. Two experiments are performed on a 7-DoF manipulator to validate the approach.
Vertex Fault-Tolerant Geometric Spanners for Weighted Points
Venue: arXiv cs.CG 2020
Year: 2020

Given a set S of n points, a weight function w to associate a non-negative weight to each point in S, a positive integer k \ge 1, and a real number ε> 0, we present algorithms for computing a spanner network G(S, E) for the metric space (S, d_w) induced by the weighted points in S. The weighted distance function d_w on the set S of points is defined as follows: for any p, q \in S, d_w(p, q) is equal to w(p) + d_π(p, q) + w(q) if p \ne q, otherwise, d_w(p, q) is 0. Here, d_π(p, q) is the Euclidean distance between p and q if points in S are in \mathbb{R}^d, otherwise, it is the geodesic (Euclidean) distance between p and q. The following are our results: (1) When the weighted points in S are located in \mathbb{R}^d, we compute a k-vertex fault-tolerant (4+ε)-spanner network of size O(k n). (2) When the weighted points in S are located in the relative interior of the free space of a polygonal domain \cal P, we detail an algorithm to compute a k-vertex fault-tolerant (4+ε)-spanner network with O(\frac{kn\sqrt{h+1}}{ε^2} \lg{n}) edges. Here, h is the number of simple polygonal holes in \cal P. (3) When the weighted points in S are located on a polyhedral terrain \cal T, we propose an algorithm to compute a k-vertex fault-tolerant (4+ε)-spanner network, and the number of edges in this network is O(\frac{kn}{ε^2} \lg{n}).
Domain Adaptive Person Re-Identification via Coupling Optimization
Venue: arXiv cs.CV 2020
Year: 2020

Domain adaptive person Re-Identification (ReID) is challenging owing to the domain gap and shortage of annotations on target scenarios. To handle those two challenges, this paper proposes a coupling optimization method including the Domain-Invariant Mapping (DIM) method and the Global-Local distance Optimization (GLO), respectively. Different from previous methods that transfer knowledge in two stages, the DIM achieves a more efficient one-stage knowledge transfer by mapping images in labeled and unlabeled datasets to a shared feature space. GLO is designed to train the ReID model with unsupervised setting on the target domain. Instead of relying on existing optimization strategies designed for supervised training, GLO involves more images in distance optimization, and achieves better robustness to noisy label prediction. GLO also integrates distance optimizations in both the global dataset and local training batch, thus exhibits better training efficiency. Extensive experiments on three large-scale datasets, i.e., Market-1501, DukeMTMC-reID, and MSMT17, show that our coupling optimization outperforms state-of-the-art methods by a large margin. Our method also works well in unsupervised training, and even outperforms several recent domain adaptive methods.
Deep Learning-based Cattle Activity Classification Using Joint Time-frequency Data Representation
Venue: arXiv cs.LG 2020
Year: 2020

Automated cattle activity classification allows herders to continuously monitor the health and well-being of livestock, resulting in increased quality and quantity of beef and dairy products. In this paper, a sequential deep neural network is used to develop a behavioural model and to classify cattle behaviour and activities. The key focus of this paper is the exploration of a joint time-frequency domain representation of the sensor data, which is provided as the input to the neural network classifier. Our exploration is based on a real-world data set with over 3 million samples, collected from sensors with a tri-axial accelerometer, magnetometer and gyroscope, attached to collar tags of 10 dairy cows and collected over a one month period. The key results of this paper is that the joint time-frequency data representation, even when used in conjunction with a relatively basic neural network classifier, can outperform the best cattle activity classifiers reported in the literature. With a more systematic exploration of neural network classifier architectures and hyper-parameters, there is potential for even further improvements. Finally, we demonstrate that the time-frequency domain data representation allows us to efficiently trade-off a large reduction of model size and computational complexity for a very minor reduction in classification accuracy. This shows the potential for our classification approach to run on resource-constrained embedded and IoT devices.
Adversarial Skill Learning for Robust Manipulation
Venue: arXiv cs.RO 2020
Year: 2020

Deep reinforcement learning has made significant progress in robotic manipulation tasks and it works well in the ideal disturbance-free environment. However, in a real-world environment, both internal and external disturbances are inevitable, thus the performance of the trained policy will dramatically drop. To improve the robustness of the policy, we introduce the adversarial training mechanism to the robotic manipulation tasks in this paper, and an adversarial skill learning algorithm based on soft actor-critic (SAC) is proposed for robust manipulation. Extensive experiments are conducted to demonstrate that the learned policy is robust to internal and external disturbances. Additionally, the proposed algorithm is evaluated in both the simulation environment and on the real robotic platform.
HAVEN: A Unity-based Virtual Robot Environment to Showcase HRI-based Augmented Reality
Venue: arXiv cs.RO 2020
Year: 2020

Due to the COVID-19 pandemic, conducting Human-Robot Interaction (HRI) studies in person is not permissible due to social distancing practices to limit the spread of the virus. Therefore, a virtual reality (VR) simulation with a virtual robot may offer an alternative to real-life HRI studies. Like a real intelligent robot, a virtual robot can utilize the same advanced algorithms to behave autonomously. This paper introduces HAVEN (HRI-based Augmentation in a Virtual robot Environment using uNity), a VR simulation that enables users to interact with a virtual robot. The goal of this system design is to enable researchers to conduct HRI Augmented Reality studies using a virtual robot without being in a real environment. This framework also introduces two common HRI experiment designs: a hallway passing scenario and human-robot team object retrieval scenario. Both reflect HAVEN's potential as a tool for future AR-based HRI studies.
Accelerating combinatorial filter reduction through constraints
Venue: arXiv cs.RO 2020
Year: 2020

Reduction of combinatorial filters involves compressing state representations that robots use. Such optimization arises in automating the construction of minimalist robots. But exact combinatorial filter reduction is an NP-complete problem and all current techniques are either inexact or formalized with exponentially many constraints. This paper proposes a new formalization needing only a polynomial number of constraints, and characterizes these constraints in three different forms: nonlinear, linear, and conjunctive normal form. Empirical results show that constraints in conjunctive normal form capture the problem most effectively, leading to a method that outperforms the others. Further examination indicates that a substantial proportion of constraints remain inactive during iterative filter reduction. To leverage this observation, we introduce just-in-time generation of such constraints, which yields improvements in efficiency and has the potential to minimize large filters.
Learning with Molecules beyond Graph Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

We demonstrate a deep learning framework which is inherently based in the highly expressive language of relational logic, enabling to, among other things, capture arbitrarily complex graph structures. We show how Graph Neural Networks and similar models can be easily covered in the framework by specifying the underlying propagation rules in the relational logic. The declarative nature of the used language then allows to easily modify and extend the propagation schemes into complex structures, such as the molecular rings which we choose for a short demonstration in this paper.
Practical and Ethical Considerations in the Effective use of Emotion and Sentiment Lexicons
Venue: arXiv cs.CL 2020
Year: 2020

Lexicons of word-emotion associations are widely used in research and real-world applications. As part of my research, I have created several such lexicons (e.g., the NRC Emotion Lexicon). This paper outlines some practical and ethical considerations involved in the effective use of these lexical resources.
Curse of Small Sample Size in Forecasting of the Active Cases in COVID-19 Outbreak
Venue: arXiv cs.LG 2020
Year: 2020

During the COVID-19 pandemic, a massive number of attempts on the predictions of the number of cases and the other future trends of this pandemic have been made. However, they fail to predict, in a reliable way, the medium and long term evolution of fundamental features of COVID-19 outbreak within acceptable accuracy. This paper gives an explanation for the failure of machine learning models in this particular forecasting problem. The paper shows that simple linear regression models provide high prediction accuracy values reliably but only for a 2-weeks period and that relatively complex machine learning models, which have the potential of learning long term predictions with low errors, cannot achieve to obtain good predictions with possessing a high generalization ability. It is suggested in the paper that the lack of a sufficient number of samples is the source of low prediction performance of the forecasting models. The reliability of the forecasting results about the active cases is measured in terms of the cross-validation prediction errors, which are used as expectations for the generalization errors of the forecasters. To exploit the information, which is of most relevant with the active cases, we perform feature selection over a variety of variables. We apply different feature selection methods, namely the Pairwise Correlation, Recursive Feature Selection, and feature selection by using the Lasso regression and compare them to each other and also with the models not employing any feature selection. Furthermore, we compare Linear Regression, Multi-Layer Perceptron, and Long-Short Term Memory models each of which is used for prediction active cases together with the mentioned feature selection methods. Our results show that the accurate forecasting of the active cases with high generalization ability is possible up to 3 days only because of the small sample size of COVID-19 data.
Sliding on Manifolds: Geometric Attitude Control with Quaternions
Venue: arXiv cs.RO 2020
Year: 2020

This work proposes a quaternion-based sliding variable that describes exponentially convergent error dynamics for any forward complete desired attitude trajectory. The proposed sliding variable directly operates on the non-Euclidean space formed by quaternions and explicitly handles the double covering property to enable global attitude tracking when used in feedback. In-depth analysis of the sliding variable is provided and compared to others in the literature. Several feedback controllers including nonlinear PD, robust, and adaptive sliding control are then derived. Simulation results of a rigid body with uncertain dynamics demonstrate the effectiveness and superiority of the approach.
Cost-Aware Type Theory
Venue: arXiv cs.PL 2020
Year: 2020

Although computational complexity is a fundamental aspect of program behavior, it is often at odds with common type theoretic principles such as function extensionality, which identifies all functions with the same $\textit{input-output}$ behavior. We present a computational type theory called $\mathbf{CATT}$ that has a primitive notion of cost (the number of evaluation steps). We introduce a new dependent function type "funtime" whose semantics can be viewed as a cost-aware version of function extensionality. We prove a collection of lemmas for $\mathbf{CATT}$, including a novel introduction rule for the new funtime type. $\mathbf{CATT}$ can be simultaneously viewed as a framework for analyzing computational complexity of programs and as the beginnings of a semantic foundation for characterizing feasible mathematical proofs.